CtIP links DNA double-strand break sensing to resection.

In response to DNA double-strand breaks (DSBs), cells sense the DNA lesions and then activate the protein kinase ATM. Subsequent DSB resection produces RPA-coated ssDNA that is essential for activation of the DNA damage checkpoint and DNA repair by homologous recombination (HR). However, the biochemical mechanism underlying the transition from DSB sensing to resection remains unclear. Using Xenopus egg extracts and human cells, we show that the tumor suppressor protein CtIP plays a critical role in this transition. We find that CtIP translocates to DSBs, a process dependent on the DSB sensor complex Mre11-Rad50-NBS1, the kinase activity of ATM, and a direct DNA-binding motif in CtIP, and then promotes DSB resection. Thus, CtIP facilitates the transition from DSB sensing to processing: it does so by binding to the DNA at DSBs after DSB sensing and ATM activation and then promoting DNA resection, leading to checkpoint activation and HR.

Simulating Cerebral Edema and Ischemia After Traumatic Acute Subdural Hematoma Using Triphasic Swelling Biomechanics.

Poor outcome following traumatic acute subdural hematoma (ASDH) is associated with the severity of the primary injury and secondary injury including cerebral edema and ischemia. However, the underlying secondary injury mechanism contributing to elevated intracranial pressure (ICP) and high mortality rate remains unclear. Cerebral edema occurs in response to the exposure of the intracellular fixed charge density (FCD) after cell death, causing ICP to increase. The increased ICP from swollen tissue compresses blood vessels in adjacent tissue, restricting blood flow and leading to ischemic damage. We hypothesize that the mass occupying effect of ASDH exacerbates the ischemic injury, leading to ICP elevation, which is an indicator of high mortality rate in the clinic. Using FEBio (febio.org) and triphasic swelling biomechanics, this study modeled clinically relevant ASDHs and simulated post-traumatic brain swelling and ischemia to predict ICP. Results showed that common convexity ASDH significantly increased ICP by exacerbating ischemic injury, and surgical removal of the convexity ASDH may control ICP by preventing ischemia progression. However, in cases where the primary injury is very severe, surgical intervention alone may not effectively decrease ICP, as the contribution of the hematoma to the elevated ICP is insignificant. In addition, interhemispheric ASDH, located between the cerebral hemispheres, does not significantly exacerbate ischemia, supporting the conservative surgical management generally recommended for interhemispheric ASDH. The joint effect of the mass occupying effect of the blood clot and resulting ischemia contributes to elevated ICP which may increase mortality. Our novel approach may improve the fidelity of predicting patient outcome after motor vehicle crashes and traumatic brain injuries due to other causes.

Region-Dependent Mechanical Properties of Human Brain Tissue Under Large Deformations Using Inverse Finite Element Modeling.

This study aims to facilitate intracranial simulation of traumatic events by determining the mechanical properties of different anatomical structures of the brain. Our experimental indentation paradigm used fresh, post-operative human tissue, which is highly advantageous in determining mechanical properties without being affected by postmortem time. This study employed an inverse finite element approach coupled with experimental indentation data to characterize mechanical properties of the human hippocampus (CA1, CA3, dentate gyrus), cortex white matter, and cortex grey matter. We determined that an uncoupled viscoelastic Ogden constitutive formulation was most appropriate to represent the mechanical behavior of these different regions of brain. Anatomical regions were significantly different in their mechanical properties. The cortex white matter was stiffer than cortex grey matter, and the CA1 and dentate gyrus were both stiffer than cortex grey matter. Although no sex dependency was observed, there were trends indicating that male brain regions were generally stiffer than corresponding female regions. In addition, there were no statistically significant age dependent differences. This study provides a structure-specific description of fresh human brain tissue mechanical properties, which will be an important step toward explicitly modeling the heterogeneity of brain tissue deformation during TBI through finite element modeling.

Region-Dependent Viscoelastic Properties of Human Brain Tissue Under Large Deformations.

This study characterizes the mechanical properties of human brain tissue resected during the course of surgery under multistep indentation loading up to 30% strain. The experimental characterization using fresh, post-operative, human brain tissue is highly advantageous since postmortem times can affect its biomechanical behavior. Although the quasilinear theory of viscoelasticity (QLV) approach has been widely used to model brain tissue mechanical properties, our analysis concluded that the linear viscoelastic approach provided a better fit to the experimental data overall. The only statistically significant regional difference in observed stiffness was between the cortex gray and dentate gyrus. There were no statistically significant age or sex dependent differences, although the data suggested that the cortex white matter in males was stiffer than that in females. Our results can help improve the accuracy of finite element models of brain tissue deformation to predict its response to traumatic brain injury.

Prediction of probability of fatality due to brain injury in traffic accidents.

Objective: Fatal brain injuries result from physiological changes in brain tissues, subsequent to primary damage caused by head impact. Although efforts have been made in past studies to estimate the probability of brain injury, none of them involved prediction of such physiological changes. The goal of this study was to evaluate the fatality prediction capability of a novel approach that predicts an increase in intracranial pressure (ICP) due to primary head injury to estimate the fatality rate using clinical data that correlate ICP with fatality rate. Methods: A total of 12 sets of head acceleration time histories were used to represent no, severe, and fatal brain injury. They were obtained from the literature presenting head kinematics data in noninjurious volunteer sled tests or from accident reconstruction for severe and fatal injury cases. These were first applied to a Global Human Body Models Consortium (GHBMC) head-brain model to predict nodal displacement time histories of the brain, which were then fed into FEBio to predict ICP. A Weibull distribution was applied to the data for the relationship between fatality rate and ICP obtained from a clinical paper to estimate fatality rate from ICP (procedure A). Fatality rate was also estimated by applying the temporal and spatial maximum value of maximum principal strain (MPSmax) obtained from the GHBMC simulation to an injury probability function for MPSmax (procedure B). Estimated fatality rates were compared between the 2 procedures. Results: Both procedures estimated higher average fatality rate for higher injury severity. The average fatality rate for procedure A without ischemia representation and procedure B was 72.4 and 51.0% for the fatal injury group and 8.2 and 21.7% for the severe injury group, respectively, showing that procedure A provides more distinct classification between fatal and nonfatal brain injury. It was also found that representation of ischemia in procedure A provides results sensitive to injury severity and impact conditions, requiring further validation of the initial estimate for the relationship between brain compression and ischemic cell death. Conclusions: Prediction of the probability of fatality by means of a combination of simulations of the primary brain deformation and subsequent ICP increase was found to be more distinct compared to the prediction of primary injury alone combined with the injury probability function from a past study in the select 12 head impact cases.

Simulating cerebral edema and delayed fatality after traumatic brain injury using triphasic swelling biomechanics.

Objectives: Contemporary finite element (FE) models, like that from the Global Human Body Models Consortium (GHBMC), have been useful for developing safety systems to reduce the severity of injuries in motor vehicle crashes (MVCs), including traumatic brain injury (TBI). However, not all injury occurs during the MVC. Cerebral edema after TBI contributes to mortality by increasing intracranial pressure (ICP) and preventing adequate cerebral blood supply. The focus of this study was to model post-traumatic cerebral edema and subsequent mortality due to increased ICP.Methods: Brain tissue swells in a manner consistent with triphasic biomechanics, which models biological tissues as a charged deformable porous solid matrix (fixed charge density [FCD]), a solvent, and monovalent counter-ions (cerebrospinal fluid). Fluid uptake into the brain is driven by the Gibbs-Donnan osmotic pressure as the FCD is exposed when cells die. Post-TBI edema was simulated in FEBio (febio.org), which includes triphasic material formulations.The GHBMC mesh was imported into FEBio, and each element was assigned a FCD to represent impact-related cell death based on its maximum principal strain (MPS) experienced during the crash-simulation using the stock GHBMC model and LS-DYNA. The ensuing pathophysiology was simulated in FEBio in two steps. First, the brain swelled in response to exposure of FCD, causing some adjacent elements to compress as fluid was redistributed. Biologically, the compression was assumed to reduce blood flow and cause ischemic cell death, represented by additional exposure of FCD, swelling, and increased ICP. Using published prognostic models of clinical outcome, mortality was predicted based on ICP.Results: Post-traumatic volume ratio of elements ranged from less than 30% (compaction) to greater than 200% (swelling). Predicted ICP values for a fatal impact were as high as 8.55 kPa (64.1 mmHg), which is associated with a 99% probability of death.Conclusion: To the best of our knowledge, this is the first study to simulate post-traumatic brain swelling to predict outcome. By incorporating swelling, ischemia, and cell death, our novel approach may improve fidelity of predicting outcome after MVCs. A strength of our approach is relying on the validated GHBMC model to predict brain deformation in the crash-scenario. The main goal of the current study was to demonstrate feasibility of simulating post-injury swelling using triphasic biomechanics. We successfully predicted clinically relevant increases in ICP that suggest a high likelihood of death when simulating a fatal impact scenario, however, more validation of our methodology is needed.