Studies were heterogeneous in design and had significant limitations, with most failing to adjust for confounding factors in cerebrovascular/CBF response. This review highlights the significant knowledge gap on the cerebrovascular/CBF effects of PE administration in TBI, calling for further study on the impact of PE on the cerebrovasculature both in vivo and in experimental settings.Sports-related concussions (SRCs) are a public health concern across the United States, and they lead to neurological sequelae that can last long after the event itself. Concussive convulsions at the time of injury are common and rarely require additional workup or treatment. Post-traumatic epilepsy (PTE), however, is a rare phenomenon that can develop after traumatic brain injury and must be managed with adequate medical therapy. Herein we present the case of a 15-year-old football player who developed PTE after an SRC.  https://www.selleckchem.com/products/apoptozole.html  This condition must be identified through proper education of sports clinicians and those involved in care and management of athletes.Polytrauma and traumatic brain injury (TBI) frequently co-occur and outcomes are routinely measured by the Glasgow Outcome Scale-Extended (GOSE). Polytrauma may confound GOSE measurement of TBI-specific outcomes. Adult patients with TBI from the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study had presented to a Level 1 trauma center after injury, received head computed tomography (CT) within 24 h, and completed the GOSE at 3 months and 6 months post-injury. Polytrauma was defined as an Abbreviated Injury Score (AIS) ≥3 in any extracranial region. Univariate regressions were performed using known GOSE clinical cutoffs. Multi-variable regressions were performed for the 3- and 6-month GOSE, controlling for known demographic and injury predictors. Of 361 subjects (age 44.9 ± 18.9 years, 69.8% male), 69 (19.1%) suffered polytrauma. By Glasgow Coma Scale (GCS) assessment, 80.1% had mild, 5.8% moderate, and 14.1% severe TBI. On univariate logistic regrries, will generate better TBI outcomes assessment tools.Spinal cord injury (SCI) is associated with obesity and is a risk factor for type 2 diabetes mellitus (T2DM). Immobilization, muscle atrophy, obesity, and loss of sympathetic innervation to the liver are believed to contribute to risks of these abnormalities. Systematic study of the mechanisms underlying SCI-induced metabolic disorders has been limited by a lack of animal models of insulin resistance following SCI. Therefore, the effects of a high-fat diet (HFD), which causes weight gain and glucose intolerance in neurologically intact mice, was tested in mice that had undergone a spinal cord transection at thoracic vertebra 10 (T10) or a sham-transection. At 84 days after surgery, Sham-HFD and SCI-HFD mice showed impaired intraperitoneal glucose tolerance when compared with Sham control (Sham-Con) or SCI control (SCI-Con) mice fed a standard control chow. Glucose tolerance in SCI-Con mice was comparable to that of Sham-Con mice. The mass of paralyzed skeletal muscle, liver, and epididymal, inguinal, and omental fat deposits were lower in SCI versus Sham groups, with lower liver mass present in SCI-HFD versus SCI-Con animals. SCI also produced sublesional bone loss, with no differences between SCI-Con and SCI-HFD groups. The results suggest that administration of a HFD to mice after SCI may provide a model to better understand mechanisms leading to insulin resistance post-SCI, as well as an approach to study pathogenesis of glucose intolerance that is independent of obesity.The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.Emergency departments (EDs) are eerily quiet for illnesses apart from COVID-19. In this short communication, we assessed the effect of COVID-19 on ED attendance rates for traumatic brain injury (TBI). Data were collected from all consecutive patients with TBI attending our hospital (Haaglanden Medical Center, The Hague, The Netherlands) during the first 3 weeks of the Dutch lockdown (from March 18 to April 6) and for the same period last year. We observed a 36% decrease in ED attendance for TBI since the beginning of the SARS-CoV-2 pandemic (91 vs. 143). Patients who presented during the lockdown were significantly older compared with the patients who visited the ED in the previous year (72 vs. 57, p = 0.01). No other significant differences were found.