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1. Reduced Reelin Expression in the Hippocampus after Traumatic Brain Injury

   https://doi.org/10.3390/biom10070975  

   Dal Pozzo, Valentina; Crowell, Beth; Briski, Nicholas; Crockett, David P.; D’Arcangelo, Gabriella (July 2020, Biomolecules)

   null (Ed.)

   Traumatic brain injury (TBI) is a relatively common occurrence following accidents or violence, and often results in long-term cognitive or motor disability. Despite the high health cost associated with this type of injury, presently there are no effective treatments for many neurological symptoms resulting from TBI. This is due in part to our limited understanding of the mechanisms underlying brain dysfunction after injury. In this study, we used the mouse controlled cortical impact (CCI) model to investigate the effects of TBI, and focused on Reelin, an extracellular protein that critically regulates brain development and modulates synaptic activity in the adult brain. We found that Reelin expression decreases in forebrain regions after TBI, and that the number of Reelin-expressing cells decrease specifically in the hippocampus, an area of the brain that plays an important role in learning and memory. We also conducted in vitro experiments using mouse neuronal cultures and discovered that Reelin protects hippocampal neuronal cells from glutamate-induced neurotoxicity, a well-known secondary effect of TBI. Together our findings suggest that the loss of Reelin expression may contribute to neuronal death in the hippocampus after TBI, and raise the possibility that increasing Reelin levels or signaling activity may promote functional recovery. 

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2. A Strain Rate-Dependent Constitutive Model for Göttingen Minipig Cerebral Arteries

   https://doi.org/10.1115/1.4053796  

   Pearson, Noah; Boiczyk, Gregory M.; Kote, Vivek Bhaskar; Sundaramurthy, Aravind; Subramaniam, Dhananjay Radhakrishnan; Rubio, Jose E.; Unnikrishnan, Ginu; Reifman, Jaques; Monson, Kenneth (August 2022, Journal of Biomechanical Engineering)

   Abstract Computational simulations of traumatic brain injury (TBI) are commonly used to advance understanding of the injury–pathology relationship, tissue damage thresholds, and design of protective equipment such as helmets. Both human and animal TBI models have developed substantially over recent decades, partially due to the inclusion of more detailed brain geometry and representation of tissues like cerebral blood vessels. Explicit incorporation of vessels dramatically affects local strain and enables researchers to investigate TBI-induced damage to the vasculature. While some studies have indicated that cerebral arteries are rate-dependent, no published experimentally based, rate-sensitive constitutive models of cerebral arteries exist. In this work, we characterize the mechanical properties of axially failed porcine arteries, both quasi-statically (0.01 s−1) and at high rate (>100 s−1), and propose a rate-sensitive model to fit the data. We find that the quasi-static and high-rate stress–stretch curves become significantly different (p < 0.05) above a stretch of 1.23. We additionally find a significant change in both failure stretch and stress as a result of strain rate. The stress–stretch curve is then modeled as a Holzapfel–Gasser–Ogden material, with a Prony series added to capture the effects of viscoelasticity. Ultimately, this paper demonstrates that rate dependence should be considered in the material properties of cerebral arteries undergoing high strain-rate deformations and provides a ready-to-use model for finite element implementation. 

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3. Investigating the Role of Neck Muscle Activation and Neck Damping Characteristics in Brain Injury Mechanism

   https://doi.org/10.1101/2023.11.15.567289  

   Bahreinizad, Hossein; Chowdhury, Suman K (November 2023, bioRxiv)

   Abstract PurposeThis study aimed to investigate the role of neck muscle activity and neck damping characteristics in traumatic brain injury (TBI) mechanisms. MethodsWe used a previously validated head-neck finite element (FE) model that incorporates various components such as scalp, skull, cerebrospinal fluid, brain, muscles, ligaments, cervical vertebrae, and intervertebral discs. Impact scenarios included a Golf ball impact, NBDL linear acceleration, and Zhang’s linear and rotational accelerations. Three muscle activation strategies (no-activation, low-to-medium, and high activation levels) and two neck damping levels by perturbing intervertebral disc properties (high: hyper-viscoelastic and low: hyper-elastic) strategies were examined. We employed Head Injury Criterion (HIC), Brain Injury Criterion (BrIC), and maximum principal strain (MPS) as TBI measures. ResultsIncreased neck muscle activation consistently reduced the values of all TBI measures in Golf ball impact (HIC: 4%-7%, BrIC: 11%-25%, and MPS (occipital): 27%-50%) and NBDL study (HIC: 64%-69%, BrIC: 3%-9%, and MPS (occipital): 6%-19%) simulations. In Zhang’s study, TBI metric values decreased with the increased muscle activation from no-activation to low-to-medium (HIC: 74%-83%, BrIC: 27%-27%, and MPS (occipital): 60%-90%) and then drastically increased with further increases to the high activation level (HIC: 288%-507%, BrIC: 1%-25%, and MPS (occipital): 23%-305%). Neck damping changes from low to high decreased all values of TBI metrics, particularly in Zhang’s study (up to 40% reductions). ConclusionOur results underscore the pivotal role of neck muscle activation and neck damping in TBI mitigation and holds promise to advance effective TBI prevention and protection strategies for diverse applications. 

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4. Assessment of the Behaviors of an In Vitro Brain Model On-Chip under Shockwave Impacts

   https://doi.org/10.1021/acsami.4c08026  

   Rubby, Md Fazlay; Fonder, Catherine; Uchayash, Sajid; Liang, Xiaogan; Sakaguchi, Donald S; Que, Long (July 2024, ACS Applied Materials & Interfaces)

   Herein we report the assessment of the effects of shockwave (SW) impacts on adult rat hippocampal progenitor cell (AHPC) neurospheres (NSs), which are used as in vitro brain models, for enhancing our understanding of the mechanisms of traumatic brain injury (TBI). The assessment has been achieved by using culture dishes and a new microchip. The microchip allows the chemicals released from the brain models cultured inside the cell culture chamber under SW impacts to diffuse to the nano sensors in adjacent sensor chambers through built-in diffusion barriers, which are used to prevent the cells from entering the sensor chambers, thereby mitigating the biofouling issues of the sensor surface. Experiments showed the negative impact of the SW on the viability, proliferation, and differentiation of the cells within the NSs. A qPCR gene expression analysis was performed and appeared to confirm some of the immunocytochemistry (ICC) results. Finally, we demonstrated that the microchip can be used to monitor lactate dehydrogenase (LDH) released from the AHPC-NSs subjected to SW impacts. As expected, LDH levels changed when AHPC-NSs were injured by SW impacts, verifying this chip can be used for assessing the degrees of injuries of AHPC-NSs by monitoring LDH levels. Taken together, these results suggest the feasibility of using the chip to better understand the interactions between SW impacts and in vitro brain models, paving a way for potentially establishing in vitro TBI models on a chip. 

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5. Subspace Clustering of Physiological Data From Acute Traumatic Brain Injury Patients: Retrospective Analysis Based on the PROTECT III Trial

   https://doi.org/10.2196/24698  

   Ehsani, Sina; Reddy, Chandan K; Foreman, Brandon; Ratcliff, Jonathan; Subbian, Vignesh (January 2021, JMIR Biomedical Engineering)

   null (Ed.)

   Background With advances in digital health technologies and proliferation of biomedical data in recent years, applications of machine learning in health care and medicine have gained considerable attention. While inpatient settings are equipped to generate rich clinical data from patients, there is a dearth of actionable information that can be used for pursuing secondary research for specific clinical conditions. Objective This study focused on applying unsupervised machine learning techniques for traumatic brain injury (TBI), which is the leading cause of death and disability among children and adults aged less than 44 years. Specifically, we present a case study to demonstrate the feasibility and applicability of subspace clustering techniques for extracting patterns from data collected from TBI patients. Methods Data for this study were obtained from the Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment–Phase III (PROTECT III) trial, which included a cohort of 882 TBI patients. We applied subspace-clustering methods (density-based, cell-based, and clustering-oriented methods) to this data set and compared the performance of the different clustering methods. Results The analyses showed the following three clusters of laboratory physiological data: (1) international normalized ratio (INR), (2) INR, chloride, and creatinine, and (3) hemoglobin and hematocrit. While all subclustering algorithms had a reasonable accuracy in classifying patients by mortality status, the density-based algorithm had a higher F1 score and coverage. Conclusions Clustering approaches serve as an important step for phenotype definition and validation in clinical domains such as TBI, where patient and injury heterogeneity are among the major reasons for failure of clinical trials. The results from this study provide a foundation to develop scalable clustering algorithms for further research and validation. 

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