Articles: traumatic-brain-injuries.
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Journal of neurotrauma · Jul 2023
ADAM17 aggravates the inflammatory response by modulating microglia polarization through the TGF-β1/Smad pathway following experimental traumatic brain injury.
Microglia-mediated neuroinflammatory responses play important roles in secondary neurological injury after traumatic brain injury (TBI). The TGF-β pathway participates in the regulation of M1/M2 phenotype transformation of microglia. TGF-β can activate the Smad pathway by binding to TGF-βRs, which is regulated by the cleavage function of A disintegrin and metalloproteinase 17 (ADAM17). ⋯ The neuroprotective effect of ADAM17 inhibition was related to a shift from the M1 microglial phenotype to the M2 microglial phenotype, thus reducing TBI-induced neuroinflammation. ADAM17 inhibition increased expression of TGF-βRs on the microglia membrane, promoted formation of TGF-β1/TGF-βRII complexes, and induced intranuclear translocation of Smads, which activated the TGF-β/Smad pathway. In conclusion, our study suggested that ADAM17 inhibition regulated microglia M1/M2 phenotype polarization through the TGF-β1/Smad pathway and influenced the neuroinflammatory response after TBI.
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J Neurosurg Anesthesiol · Jul 2023
Predicting Mortality Following Traumatic Brain Injury or Subarachnoid Hemorrhage: An Analysis of the Validity of Standardized Mortality Ratios Obtained From the APACHE II and ICNARCH-2018 Models.
Standardized mortality ratios (SMRs), calculated using the Acute Physiology, Age, Chronic Health Evaluation II (APACHE II) and Intensive Care National Audit and Research Centre H-2018 (ICNARC H-2018 ) risk prediction models, are widely used in UK intensive care units (ICUs) to measure and compare the quality of critical care delivery. Both models incorporate an assumption of Glasgow Coma Score (GCS) if an actual GCS without sedation is not recordable in the first 24 hours after ICU admission. This study assesses the validity of the APACHE II and ICNARC H-2018 models to predict mortality in ICU patients with traumatic brain injury (TBI) or aneurysmal subarachnoid hemorrhage (aSAH) in whom GCS is related to outcomes. ⋯ The APACHE II and ICNARC H-2018 models predicted mortality well for the overall TBI/aSAH ICU population but underpredicted mortality when GCS was ≤8 or "unrecordable." This raises questions about the accuracy of these risk prediction models in TBI/aSAH patients and their use to evaluate treatments and compare outcomes between centers.
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Journal of neurotrauma · Jul 2023
Observational StudyWithdrawal of life sustaining therapies in children with severe traumatic brain injury.
Neuroprognostication in severe traumatic brain injury (sTBI) is challenging and occurs in critical care settings to determine withdrawal of life-sustaining therapies (WLST). However, formal pediatric sTBI neuroprognostication guidelines are lacking, brain death criteria vary, and dilemmas regarding WLST persist, which lead to institutional differences. We studied WLST practice and outcome in pediatric sTBI to provide insight into WLST-associated factors and survivor recovery trajectory ≥1 year post-sTBI. ⋯ Median survivor PCPC score improved from 4 to 2 with no vegetative patients 1 year post-sTBI. Our findings show the WLST decision process was multi-disciplinary and guided by specific clinical features at presentation, clinical course, and (serial) neurological diagnostic modalities, of which the testing combination was determined by case-to-case variation. This stresses the need for international guidelines to provide accurate neuroprognostication within an appropriate timeframe whereby overall survivor outcome data provides valuable context and guidance in the acute phase decision process.
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Journal of neurotrauma · Jul 2023
ReviewPutting the mind to rest: a historical foundation for rest as a treatment for traumatic brain injury.
Rest after traumatic brain injury (TBI) has been a part of clinical practice for more than a century but the use of rest as a treatment has ancient roots. In contemporary practice, rest recommendations have been significantly reduced but are still present. This advice to brain injured patients, on the face of it makes some logical sense but was not historically anchored in either theory or empirical data. ⋯ The goals and theoretical explanations for this approach have evolved and in modern conception include avoiding reinjury and reducing the metabolic demands on injured tissue. Moreover, as cellular and molecular understanding of the physiology of TBI developed, scientists and clinicians sometimes retroactively cited these new data in support of rest recommendations. Here, we trace the history of this approach and how it has been shaped by new understanding of the underlying pathology associated with brain injury.
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Journal of neurotrauma · Jul 2023
Use of support vector machines approach via ComBat harmonized diffusion tensor imaging for the diagnosis and prognosis of mild traumatic brain injury: a CENTER-TBI study.
The prediction of functional outcome after mild traumatic brain injury (mTBI) is challenging. Conventional magnetic resonance imaging (MRI) does not do a good job of explaining the variance in outcome, as many patients with incomplete recovery will have normal-appearing clinical neuroimaging. More advanced quantitative techniques such as diffusion MRI (dMRI), can detect microstructural changes not otherwise visible, and so may offer a way to improve outcome prediction. ⋯ Similar to the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD maps voxelwise approach yields statistically significant prediction scores between mTBI patients with complete and those with incomplete recovery (71.8% specificity, 66.2% F1-score and 0.71 AUC, p < 0.05), which provided a modest increase in the classification score (accuracy: 66.4%) compared with the classification based on age and sex only and ROI-wise approaches (accuracy: 61.4% and 64.7%, respectively). This study showed that ComBat harmonized FA and MD may provide additional information for diagnosis and prognosis of mTBI in a multi-modal machine learning approach. These findings demonstrate that dMRI may assist in the early detection of patients at risk of incomplete recovery from mTBI.