A computational study on brain tissue under blast: primary and tertiary blast injuries.

In this paper, a biomechanical study of a human head model exposed to blast shock waves followed by a blunt impact with the surface of the enclosing walls of a confined space is carried out. Under blast, the head may experience primary blast injury (PBI) due to exposure to the shockwaves and tertiary blast injury (TeBI) due to a possible blunt impact. We examine the brain response data in a deformable finite element head model in terms of the inflicted stress/pressure, velocity, and acceleration on the brain for several blast scenarios with different intensities. The data will be compared for open space and confined spaces. Following the initial impact of the shock front in the confined space, one can see the fluctuations in biomechanical data due to wave reflections. Although the severity of the PBI and TeBI is dependent on the situation, for the cases studied here, PBI is considerably more pronounced than TeBI in confined spaces.

Computational modeling of human head under blast in confined and open spaces: primary blast injury.

In this paper, a computational modeling for biomechanical analysis of primary blast injuries is presented. The responses of the brain in terms of mechanical parameters under different blast spaces including open, semi-confined, and confined environments are studied. In the study, the effect of direct and indirect blast waves from the neighboring walls in the confined environments will be taken into consideration. A 50th percentile finite element head model is exposed to blast waves of different intensities. In the open space, the head experiences a sudden intracranial pressure (ICP) change, which vanishes in a matter of a few milliseconds. The situation is similar in semi-confined space, but in the confined space, the reflections from the walls will create a number of subsequent peaks in ICP with a longer duration. The analysis procedure is based on a simultaneous interaction simulation of the deformable head and its components with the blast wave propagations. It is concluded that compared with the open and semi-confined space settings, the walls in the confined space scenario enhance the risk of primary blast injuries considerably because of indirect blast waves transferring a larger amount of damaging energy to the head.

Effects of neck damping properties on brain response underimpact loading.

In this paper, head-neck boundary conditions and modeling of the head are studied circumspectly. The neck is modeled using discrete elements and the head model is three-dimensional. In the study presented here, a viscoelastic foundation (i.e., foundation defined by both springs and dampers) concept is introduced to simulate the head-neck boundary conditions during the impact load to the head. Time histories of the brain response in finite element head models with a viscoelastic neck are compared with the corresponding solutions of finite element head models with an elastic neck, and without a neck. It is observed that the magnitude of peaks in the brain's response time histories, at a later stage (i.e., 6 to 15 ms) of the simulation, decreases when dampers are induced to the elastic neck. A parametric study is also conducted to examine the brain response while varying different damping coefficient values for the neck. The magnitude of peaks in the brain's response time histories for models with different neck damping coefficients is observed to maintain some form of proportionality. In other words, the magnitude of peaks in the brain's response time histories decreases with an increased damping coefficient of the neck at the later stage of the simulation (i.e., 6 to 15 ms). From the outcomes of this study, it can be determined that the head-neck boundary conditions during head impact loading are important for studying the brain's response at the later stages of the head impact.

A randomized, double-blind, placebo-controlled trial on the efficacy and tolerability of sertraline in Iranian veterans with post-traumatic stress disorder.

BACKGROUND: Unlike civilian post-traumatic stress disorder (PTSD), the efficacy of sertraline for the treatment of combat-related PTSD has not yet been proven. The present study aimed to evaluate the clinical efficacy of sertraline against combat-related PTSD in a randomized, double-blind, placebo-controlled trial. METHOD: Seventy Iranian veterans of the Iran-Iraq war who met the DSM-IV criteria for diagnosis of PTSD were randomized to receive either flexibly dosed sertraline (50-200 mg/day) (n=35, completers=32) or placebo (n=35, completers=30) for 10 weeks. Efficacy was evaluated by the Impact of Event Scale--Revised (IES-R) and the Clinical Global Impression scale--Severity (CGI-S) and Improvement (CGI-I) ratings. Responder criteria were defined as a ≥30% reduction in the IES-R total score plus a CGI-I rating of 'much' or 'very much' improved. RESULTS: On both intention-to-treat (ITT) and per protocol (completer) methods of analysis, the mean reductions in the IES-R total and subscale (re-experiencing/intrusion, avoidance/numbing and hyperarousal) scores (p<0.001) and also in the CGI-S score (p<0.01) were significantly greater in the sertraline group than in the placebo group. For the CGI-I, the mean endpoint score was significantly lower in the sertraline group than in the placebo group (p≤0.001). The number of responders in the sertraline group was significantly higher than in the placebo group (44% v. 3%, p≤0.001). Sertraline was well tolerated, with a 6% discontinuation rate as a result of adverse reactions. CONCLUSIONS: The results of this study suggest that sertraline can be an effective, safe and tolerable treatment for combat-related PTSD in Iranian veterans.

Biomechanical assessment of brain dynamic responses due to blast pressure waves.

A mechanized and integrated computational scheme is introduced to determine the human brain responses in an environment where the human head is exposed to explosions from trinitrotoluene (TNT), or other high-yield explosives, in military applications. The procedure is based on a three-dimensional (3-D) non-linear finite element method (FEM) that implements a simultaneous conduction of explosive detonation, shock wave propagation, blast-head interactions, and the confronting human head. The processes of blast propagation in the air and blast interaction with the head are modeled by an Arbitrary Lagrangian-Eulerian (ALE) multi-material FEM formulation, together with a penalty-based fluid/structure interaction (FSI) algorithm. Such a model has already been successfully validated against experimental data regarding air-free blast and plate-blast interactions. The human head model is a 3-D geometrically realistic configuration that has been previously validated against the brain intracranial pressure (ICP), as well as shear and principal strains under different impact loadings of cadaveric experimental tests of Hardy et al. [Hardy W. N., C. Foster, M. Mason, S. Chirag, J. Bishop, M. Bey, W. Anderst, and S. Tashman. A study of the response of the human cadaver head to impact. Proc. 51 ( st ) Stapp. Car Crash J. 17-80, 2007]. Different scenarios have been assumed to capture an appropriate picture of the brain response at a constant stand-off distance of nearly 80 cm from the core of the explosion, but exposed to different amounts of a highly explosive (HE) material such as TNT. The over-pressures at the vicinity of the head are in the range of about 2.4-8.7 atmosphere (atm), considering the reflected pressure from the head. The methodology provides brain ICP, maximum shear stresses and maximum principal strain within the milli-scale time frame of this highly dynamic phenomenon. While focusing on the two mechanical parameters of pressure, and also on the maximum shear stress and maximum principal strain to predict the brain injury, the research provides an assessment of the brain responses to different amounts of over-pressure. The research also demonstrates the ability to predict the ICP, as well as the stress and strain within the brain, due to such an event. The research cannot identify, however, the specific levels of ICP, stress and strain that necessarily lead to traumatic brain injury (TBI) because there is no access to experimental data regarding head-blast interactions.

A micromechanical hyperelastic modeling of brain white matter under large deformation.

A finite element based micromechanical model has been developed for analyzing and characterizing the microstructural as well as homogenized mechanical response of brain tissue under large deformation. The model takes well-organized soft tissue as a fiber-reinforced composite with nonlinear and anisotropic behavior assumption for the fiber as well as the matrix of composite matter. The procedure provides a link between the macroscopic scale and microscopic scale as brain tissue undergoes deformation. It can be used to better understand how macroscopic stresses are transferred to the microstructure or cellular structure of the brain. A repeating unit cell (RUC) is created to stand as a representative volume element (RVE) of the hyperelastic material with known properties of the constituents. The model imposes periodicity constraints on the RUC. The RUC is loaded kinematically by imposing displacements on it to create the appropriate normal and shear stresses. The homogenized response of the composite, the average stresses carried within each of the constituents, and the maximum local stresses are all obtained. For each of the normal and shear loading scenarios, the impact of geometrical variables such as the axonal fiber volume fraction and undulation of the axons are evaluated. It was found that axon undulation has significant impact on the stiffness and on how stresses were distributed between the axon and the matrix. As axon undulation increased, the maximum stress and stress in the matrix increased while the stress in the axons decreased. The axon volume fraction was found to have an impact on the tissue stiffness as higher axon volume fractions lead to higher stresses both in the composite and in the constituents. The direction of loading clearly has a large impact on how stresses are distributed amongst the constituents. This micromechanics tool provides the detailed micromechanics stresses and deformations, as well as the average homogenized behavior of the RUC, which can be efficiently used in mechanical characterization of brain tissue.

A micromechanical procedure for modelling the anisotropic mechanical properties of brain white matter.

This paper proposes a micromechanics algorithm utilising the finite element method (FEM) for the analysis of heterogeneous matter. The characterisation procedure takes the material properties of the constituents, axons and extracellular matrix (ECM) as input data. The material properties of both the axons and the matrix are assumed to have linear viscoelastic behaviour with a perfect bonding between them. The results of the modelling have been validated with experimental data with material white input from brainstem by considering the morphology of brainstem in which most axons are oriented in longitudinal direction in the form of a uniaxial fibrous composite material. The method is then employed to examine the undulations of axons within different subregions of white matter and to study the impact due to axon/matrix volume fractions. For such purposes, different unit cells composed of wavy geometries and with various volume factions have been exposed to the six possible loading scenarios. The results will clearly demonstrate the undulation and axon volume fraction impacts. In this respect, undulation affects the material stiffness heavily in the axon longitudinal direction, whereas the axons' volume fraction has a much greater impact on the mechanical properties of the white matter in general. Also the results show that the created stresses and strains in the axons and matrix under loading will be impacted by undulation change. With increase in undulation the matrix suffers higher stresses when subjected to tension, whereas axons suffer higher stresses in shear. The axons always exhibit higher stresses whereas the matrix exhibits higher strains. The evaluated time-dependent local stress and strain concentrations within a repeating unit cell of the material model are indicative of the mechanical behaviour of the white tissue under different loading scenarios.

A finite element method parametric study of the dynamic response of the human brain with different cerebrospinal fluid constitutive properties.

A major role for the cerebrospinal fluid (CSF) is to provide effective damping against sudden intracranial brain motions during dynamic head impact. This paper examines the roles of CSF properties on human brain responses under certain impact loadings. The brain is assumed to have a hyperviscoelastic material behaviour, while CSF is considered to be fluid-like elastic, viscoelastic, and nearly incompressible elastic with a low shear modulus and a high bulk modulus. A finite element parametric investigation on a head model under different scenarios of impact is conducted. In the study, the CSF material parameters are varied within the expected range of change, while other components of the head model are kept constant. The results indicate that the solutions from the modelling of CSF by a fluid-like medium are more realistic and support the findings of the experiment. The results also indicate that varying CSF properties did not have a major impact on the peak intracranial pressures but the impact on brain principal and shear strains are relatively significant. A sizeable impact on the relative motion of the brain, with respect to the skull, can also be observed.

A logistic regression model for predicting health-related quality of life in kidney transplant recipients.

BACKGROUND: To develop a logistic regression model capable of predicting health-related quality of life (HRQOL) among kidney transplant recipients and determine its accuracy. METHODS: Three groups of patients were selected: 70 healthy controls, 136 kidney transplant patients as a derivation set, and another 110 kidney transplant patients as a validation set. SF-36 score was used for HRQOL measurement. A cutoff point to define poor versus good HRQOL was calculated using the SF-36 scores of healthy controls. A logistic regression model was used to derive predictive parameters from the derivation set. The derived model was then tested among the validation set. HRQOL predictions made by the model for the patients in the validation set and the SF-36 scores were compared. We calculated sensitivity, specificity, positive and negative predictive values, and model accuracy. RESULTS: SF-36 scores below 58.8 were defined as an indication of poor HRQOL. The regression model suggested that poor HRQOL was positively associated with lower education (below high school diploma), being single or widowed, and diabetes/hypertension as etiology. It was negatively associated with younger age (<45 years) at the time of transplantation. Optimal sensitivity and specificity were achieved at a cutoff value of 0.74 for the estimated probability of poor HRQOL. Sensitivity, specificity, positive and negative predictive values, and accuracy of the model were 73%, 70%, 80%, 60%, and 72%, respectively. CONCLUSION: The suggested model can be used to predict poor posttransplant HRQOL among renal graft recipients using simple variables with acceptable accuracy. This modal can be of use in decision making in the recipients for whom achieving good HRQOL is the main aim of transplantation, to select high-risk patients and to start interventional programs to prevent a poor HRQOL.

Are all domains of quality of life poor among elderly kidney recipients?

BACKGROUND: This study sought to answer whether all domains of HRQoL are low among elderly kidney recipients. METHODS: A cross-sectional study of 162 renal transplanted subjects included group I (age<40 years, n=85), group II (age between 40 and 55 years, n=55), and group III (age >55 years, n=22). We compared the total score of the Short Form health survey (SF-36) and its eight subscales, including physical functioning (PF), social functioning (SF), role limitations due to physical health problems (RPh), role limitations due to emotional problems (REm), mental health (MH), vitality (VT), bodily pain (BP), and general health perceptions (GH) between the study groups. RESULTS: As compared to groups II and I, group III, showed significantly lower scores of REm (49.12 +/- 23.22, 63.03 +/- 26.33, 64.36 +/- 26.54, P=.08), PF (48.94 +/- 27.41, 72.69 +/- 25.54, 72.14 +/- 22.79, P=.001) and SF-36 total score (46.79 +/- 10.52, 54.77 +/- 10.66, 54.09 +/- 9.35, P=.01). There were no significant differences among SF, RPh, MH, VT, and BP. Group III reported better GH than groups II and I (52.36 +/- 9.18, 48.71 +/- 12.01, 43.50 +/- 14.81, P=.020). CONCLUSIONS: Increasing age did not result in poor health-related quality of life in all domains. The general health perception was better in the elderly, which might be due to their better coping ability.

Marital quality in kidney transplant recipients: easy to predict, hard to neglect.

BACKGROUND: Given the significant role of post-renal transplant familial support in the patient's adherence to treatment, a study into the contributors to marital quality in this population seems necessary. This study sought to identify the predictors of poor post-renal transplant marital quality. METHODS: This cross-sectional study was conducted in 2006 on 125 married kidney transplant recipients. Marital quality was evaluated using the Revised Marital Adjustment Scale (RMAS). A score below the fourth-quartile MAS score of a group of age- and sex- matched healthy controls was interpreted as poor marital relationship. Multiple logistic regression analysis was utilized to evaluate the predictors of poor marital relationship. RESULTS: The mean time interval between transplantation and assessment of marital quality was 43 +/- 15 months. Poor post-renal transplant marital quality can be predicted by the kidney transplant recipient's sex (M/F) (odds ratio [OR]; 0.31; 95% confidence interval [CI], 0.11 to 0.90; P=.031), age at transplantation (OR, 0.93; 95% CI, 0.89 to 0.98; P=.005), educational level (OR, 0.67; 95% CI, 0.44 to 1.03; P=.067), and monthly family income (OR, 2.20; 95% CI, 1.09 to 4.44; P=.028). CONCLUSION: Presenting a simple prediction model for poor post-renal transplant marital relationship, this study will make it possible to detect patients at a higher risk of poor marital quality and thus avoid treatment noncompliance. At the time of transplantation, using simple demographic variables and providing couple-based health education programs as a part of a familial approach to renal transplantation may improve the outcome of such high-risk patients.