Traumatic Brain Injuries: Manifestations, Evaluation, Management, and Outcomes.

The Psychiatric Consultation Service at Massachusetts General Hospital sees medical and surgical inpatients with comorbid psychiatric symptoms and conditions. During their twice-weekly rounds, Dr Stern and other members of the Consultation Service discuss diagnosis and management of hospitalized patients with complex medical or surgical problems who also demonstrate psychiatric symptoms or conditions. These discussions have given rise to rounds reports that will prove useful for clinicians practicing at the interface of medicine and psychiatry.Prim Care Companion CNS Disord 2024;26(3):23f03667. Author affiliations are listed at the end of this article.

Utility of serum chemokine-like factor 1 as a biomarker of severity and prognosis after severe traumatic brain injury: A prospective observational study.

BACKGROUND: Chemokine-like factor 1 (CKLF1) may be involved in the inflammatory response and secondary brain injury after severe traumatic brain injury (sTBI). We determined serum CKLF1 levels of sTBI patients to further investigate the correlation of CKLF1 levels with disease severity, functional prognosis, and 180-day mortality of sTBI. METHODS: Serum CKLF1 levels were measured at admission in 119 sTBI patients and at entry into study in 119 healthy controls. Serum CKLF levels of 50 patients were also quantified at days 1-3, 5, and 7 after admission. Glasgow coma scale (GCS) scores and Rotterdam computerized tomography (CT) classification were utilized to assess disease severity. Extended Glasgow outcome scale (GOSE) scores were recorded to evaluate function prognosis at 180 days after sTBI. Relations of serum CKLF1 levels to 180-day poor prognosis (GOSE scores of 1-4) and 180-day mortality were analyzed using univariate analysis, followed by multivariate analysis. Receiver-operating characteristic (ROC) curve was built to investigate prognostic predictive capability. RESULTS: Serum CKLF1 levels of sTBI patients increased at admission, peaked at day 2, and then gradually decreased; they were significantly higher during the 7 days after sTBI than in healthy controls. Differences of areas under ROC curve (areas under the curve [AUCs]) were not significant among the six time points. Multivariate analysis showed that serum CKLF1 levels were independently correlated with GCS scores, Rotterdam CT classification, and GOSE scores. Serum CKLF1 levels were significantly higher in non-survivors than in survivors and in poor prognosis patients than in good prognosis patients. Serum CKLF1 levels independently predicted 180-day poor prognosis and 180-day mortality, and had high 180-day prognosis and mortality predictive abilities, and their AUCs were similar to those of GCS scores and Rotterdam CT classification. Combination model containing serum CKLF1, GCS scores, and Rotterdam CT classification performed more efficiently than any of them alone in predicting mortality and poor prognosis. The models were visually described using nomograms, which were comparatively stable under calibration curve and were relatively of clinical benefit under decision curve. CONCLUSION: Serum CKLF1 levels are significantly associated with disease severity, poor 180-day prognosis, and 180-day mortality in sTBI patients. Hence, complement CKLF1 may serve as a potential prognostic biomarker of sTBI.

Serum exosomes miR-206 and miR-549a-3p as potential biomarkers of traumatic brain injury.

Traumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. However, effective diagnostic, therapeutic and prognostic biomarkers are still lacking. Our research group previously revealed through high-throughput sequencing that the serum exosomes miR-133a-3p, miR-206, and miR-549a-3p differ significantly in severe TBI (sTBI), mild or moderate TBI (mTBI), and control groups. However, convincing experimental evidence is lacking. To solve this problem, we used qPCR in this study to further verify the expression levels of serum exosomes miR-133a-3p, miR-206 and miR-549a-3p in TBI patients. The results showed that the serum exosomes miR-206 and miR-549a-3p showed good predictive value as biomarkers of TBI. In addition, in order to further verify whether serum exosomes miR-206 and miR-549a-3p can be used as potential biomarkers in patients with TBI and to understand the mechanism of their possible effects, we further determined the contents of SOD, BDNF, VEGF, VEGI, NSE and S100ß in the serum of TBI patients. The results showed that, serum exosomes miR-206 and miR-549a-3p showed good correlation with BDNF, NSE and S100ß. In conclusion, serum exosomes miR-206 and miR-549a-3p have the potential to serve as potential biomarkers in patients with TBI.

Brain tissue oxygen partial pressure monitoring and prognosis of patients with traumatic brain injury: a meta-analysis.

To assess whether monitoring brain tissue oxygen partial pressure (PbtO2) or employing intracranial pressure (ICP)/cerebral perfusion pressure (CCP)-guided management improves patient outcomes, including mortality, hospital length of stay (LOS), mean daily ICP and mean daily CCP during the intensive care unit(ICU)stay. We searched the Web of Science, EMBASE, PubMed, Cochrane Library, and MEDLINE databases until December 12, 2023. Prospective randomized controlled and cohort studies were included. A meta-analysis was performed for the primary outcome measure, mortality, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eleven studies with a total of 37,492 patients were included. The mortality in the group with PbtO2 was 29.0% (odds ratio: 0.73;95% confidence interval [CI]:0.56-0.96; P = 0.03; I = 55%), demonstrating a significant benefit. The overall hospital LOS was longer in the PbtO2 group than that in the ICP/CPP group (mean difference:2.03; 95% CI:1.03-3.02; P<0.0001; I = 39%). The mean daily ICP in the PbtO2 monitoring group was lower than that in the ICP/CPP group (mean difference:-1.93; 95% CI: -3.61 to -0.24; P = 0.03; I = 41%). Moreover, PbtO2 monitoring did not improve the mean daily CPP (mean difference:2.43; 95%CI: -1.39 to 6.25;P = 0.21; I = 56%).Compared with ICP/CPP monitoring, PbtO2 monitoring reduced the mortality and the mean daily ICP in patients with severe traumatic brain injury; however, no significant effect was noted on the mean daily CPP. In contrast, ICP/CPP monitoring alone was associated with a short hospital stay.

Refining outcome prediction after traumatic brain injury with machine learning algorithms.

Outcome after traumatic brain injury (TBI) is typically assessed using the Glasgow outcome scale extended (GOSE) with levels from 1 (death) to 8 (upper good recovery). Outcome prediction has classically been dichotomized into either dead/alive or favorable/unfavorable outcome. Binary outcome prediction models limit the possibility of detecting subtle yet significant improvements. We set out to explore different machine learning methods with the purpose of mapping their predictions to the full 8 grade scale GOSE following TBI. The models were set up using the variables: age, GCS-motor score, pupillary reaction, and Marshall CT score. For model setup and internal validation, a total of 866 patients could be included. For external validation, a cohort of 369 patients were included from Leuven, Belgium, and a cohort of 573 patients from the US multi-center ProTECT III study. Our findings indicate that proportional odds logistic regression (POLR), random forest regression, and a neural network model achieved accuracy values of 0.3-0.35 when applied to internal data, compared to the random baseline which is 0.125 for eight categories. The models demonstrated satisfactory performance during external validation in the data from Leuven, however, their performance were not satisfactory when applied to the ProTECT III dataset.

Accuracy of Early Neuroprognostication in Pediatric Severe Traumatic Brain Injury.

BACKGROUND: Children with severe traumatic brain injury (sTBI) are at risk for neurological sequelae impacting function. Clinicians are tasked with neuroprognostication to assist in decision-making. We describe a single-center study assessing clinicians' neuroprognostication accuracy. METHODS: Clinicians of various specialties caring for children with sTBI were asked to predict their patients' functioning three to six months postinjury. Clinicians were asked to participate in the study if their patient had survived but not returned to baseline between day 4 and 7 postinjury. The outcome tool utilized was the functional status scale (FSS), ranging from 6 to 30 (best-worst function). Predicted scores were compared with actual scores three to six months postinjury. Lin concordance correlation coefficients were used to estimate agreement between predicted and actual FSS. Outcome was dichotomized as good (FSS 6 to 8) or poor (FSS ≥9). Positive and negative predictive values for poor outcome were calculated. Pessimistic prognostic prediction was defined as predicted worse outcome by ≥3 FSS points. Demographic and clinical variables were collected. RESULTS: A total of 107 surveys were collected on 24 patients. Two children died. Fifteen children had complete (FSS = 6) or near-complete (FSS = 7) recovery. Mean predicted and actual FSS scores were 10.8 (S.D. 5.6) and 8.6 (S.D. 4.1), respectively. Predicted FSS scores were higher than actual scores (P < 0.001). Eight children had collective pessimistic prognostic prediction. CONCLUSIONS: Clinicians predicted worse functional outcomes, despite high percentage of patients with near-normal function at follow-up clinic. Certain patient and provider factors were noted to impact accuracy and need to be studied in larger cohorts.

Applicability and clinical utility of the German rivermead post-concussion symptoms questionnaire in proxies of children after traumatic brain injury: an instrument validation study.

BACKGROUND: The German Rivermead Post-Concussion Symptoms Questionnaire (RPQ) can be used to assess post-concussion symptoms (PCS) after traumatic brain injury (TBI) in adults, adolescents, and children. METHODS: In this study, we examined the psychometric properties of the German RPQ proxy version (N = 146) for children (8-12 years) after TBI at the item, total and scale score level. Construct validity was analyzed using rank correlations with the proxy-assessed Post-Concussion Symptoms Inventory (PCSI-P), the Patient Health Questionnaire 9 (PHQ-9), and the Generalized Anxiety Disorder Scale 7 (GAD-7). Furthermore, sensitivity testing was performed concerning subjects' sociodemographic and injury-related characteristics. Differential item functioning (DIF) was analyzed to assess the comparability of RPQ proxy ratings for children with those for adolescents. RESULTS: Good internal consistency was demonstrated regarding Cronbach's α (0.81-0.90) and McDonald's ω (0.84-0.92). The factorial validity of a three-factor model was superior to the original one-factor model. Proxy ratings of the RPQ total and scale scores were strongly correlated with the PCSI-P (ϱ = 0.50-0.69), as well as moderately to strongly correlated with the PHQ-9 (ϱ = 0.49-0.65) and the GAD-7 (ϱ = 0.44-0.64). The DIF analysis revealed no relevant differences between the child and adolescent proxy versions. CONCLUSIONS: The German RPQ proxy is a psychometrically reliable and valid instrument for assessing PCS in children after TBI. Therefore, RPQ self- and proxy-ratings can be used to assess PCS in childhood as well as along the lifespan of an individual after TBI.

[Use of invasive intracranial pressure monitoring in patients with severe traumatic brain injury]. / Ispol'zovanie invazivnogo monitoringa vnutricherepnogo davleniya u patsientov s tyazheloi cherepno-mozgovoi travmoi.

OBJECTIVE: To evaluate the effectiveness of the use of invasive intracranial pressure (ICP) monitoring on treatment outcomes in patients with severe traumatic brain injury (TBI). MATERIAL AND METHODS: We analyzed 50 case histories of patients with severe TBI who received treatment in the Krasnoyarsk Regional Clinical Hospital for the period 2021-2022. Comparisons were made between patients with and without invasive intraventricular ICP monitoring. RESULTS: With the same initial condition of patients, ICP monitoring allows for a faster and more timely response to changes in the clinical condition, which significantly affects the clinical outcome. CONCLUSION: The use of invasive ICP monitoring improves the outcome of treatment of patients with severe TBI and justifies the money spent on it.

Prevalence of Cardiovascular Conditions After Traumatic Brain Injury: A Comparison Between the Traumatic Brain Injury Model Systems and the National Health and Nutrition Examination Survey.

BACKGROUND: The purpose of this study is to compare the prevalence of self-reported cardiovascular conditions among individuals with moderate to severe traumatic brain injury (TBI) to a propensity-matched control cohort. METHODS AND RESULTS: A cross-sectional study described self-reported cardiovascular conditions (hypertension, congestive heart failure [CHF], myocardial infarction [MI], and stroke) from participants who completed interviews between January 2015 and March 2020 in 2 harmonized large cohort studies, the TBI Model Systems and the National Health and Nutrition Examination Survey. Mixed-effect logistic regression models were used to compare the prevalence of cardiovascular conditions after 1:1 propensity-score matching based on age, sex, race, ethnicity, body mass index, education level, and smoking status. The final sample was 4690 matched pairs. Individuals with TBI were more likely to report hypertension (odds ratio [OR], 1.18 [95% CI, 1.08-1.28]) and stroke (OR, 1.70 [95% CI, 1.56-1.98]) but less likely to report CHF (OR, 0.81 [95% CI, 0.67-0.99]) or MI (OR, 0.66 [95% CI, 0.55-0.79]). There was no difference in rate of CHF or MI for those ≤50 years old; however, rates of CHF and MI were lower in the TBI group for individuals >50 years old. Over 65% of individuals who died before the first follow-up interview at 1 year post-TBI were >50 years old, and those >50 years old were more likely to die of heart disease than those ≤50 years old (17.6% versus 8.6%). CONCLUSIONS: Individuals with moderate to severe TBI had an increased rate of self-reported hypertension and stroke but lower rate of MI and CHF than uninjured adults, which may be due to survival bias.

Traumatic brain injury, race, ethnicity and cognition in newly diagnosed persons with multiple sclerosis.

We sought to determine whether a history of traumatic brain injury (TBI) could explain the lower symbol digit modalities test (SDMT) scores observed among newly diagnosed multiple sclerosis (MS) and control participants identifying as Black or Hispanic versus white in the MS Sunshine Study (n = 1172). 330 (29.2 %) participants reported a history of ≥1 TBI. Accounting for TBI did not explain the significant independent associations between having MS, being Black or Hispanic and lower SDMT. The pervasive effects of systemic racism in the United States remain the best explanation for the lower SDMT scores observed in Black and Hispanic participants.

Trauma diagnostic-related target proteins and their detection techniques.

Trauma is a significant health issue that not only leads to immediate death in many cases but also causes severe complications, such as sepsis, thrombosis, haemorrhage, acute respiratory distress syndrome and traumatic brain injury, among trauma patients. Target protein identification technology is a vital technique in the field of biomedical research, enabling the study of biomolecular interactions, drug discovery and disease treatment. It plays a crucial role in identifying key protein targets associated with specific diseases or biological processes, facilitating further research, drug design and the development of treatment strategies. The application of target protein technology in biomarker detection enables the timely identification of newly emerging infections and complications in trauma patients, facilitating expeditious medical interventions and leading to reduced post-trauma mortality rates and improved patient prognoses. This review provides an overview of the current applications of target protein identification technology in trauma-related complications and provides a brief overview of the current target protein identification technology, with the aim of reducing post-trauma mortality, improving diagnostic efficiency and prognostic outcomes for patients.

Utilizing ultra-early continuous physiologic data to develop automated measures of clinical severity in a traumatic brain injury population.

Determination of prognosis in the triage process after traumatic brain injury (TBI) is difficult to achieve. Current severity measures like the Trauma and injury severity score (TRISS) and revised trauma score (RTS) rely on additional information from the Glasgow Coma Scale (GCS) and the Injury Severity Score (ISS) which may be inaccurate or delayed, limiting their usefulness in the rapid triage setting. We hypothesized that machine learning based estimations of GCS and ISS obtained through modeling of continuous vital sign features could be used to rapidly derive an automated RTS and TRISS. We derived variables from electrocardiograms (ECG), photoplethysmography (PPG), and blood pressure using continuous data obtained in the first 15 min of admission to build machine learning models of GCS and ISS (ML-GCS and ML-ISS). We compared the TRISS and RTS using ML-ISS and ML-GCS and its value using the actual ISS and GCS in predicting in-hospital mortality. Models were tested in TBI with systemic injury (head abbreviated injury scale (AIS) ≥ 1), and isolated TBI (head AIS ≥ 1 and other AIS ≤ 1). The area under the receiver operating characteristic curve (AUROC) was used to evaluate model performance. A total of 21,077 cases (2009-2015) were in the training set. 6057 cases from 2016 to 2017 were used for testing, with 472 (7.8%) severe TBI (GCS 3-8), 223 (3.7%) moderate TBI (GCS 9-12), and 5913 (88.5%) mild TBI (GCS 13-15). In the TBI with systemic injury group, ML-TRISS had similar AUROC (0.963) to TRISS (0.965) in predicting mortality. ML-RTS had AUROC (0.823) and RTS had AUROC 0.928. In the isolated TBI group, ML-TRISS had AUROC 0.977, and TRISS had AUROC 0.983. ML-RTS had AUROC 0.790 and RTS had AUROC 0.957. Estimation of ISS and GCS from machine learning based modeling of vital sign features can be utilized to provide accurate assessments of the RTS and TRISS in a population of TBI patients. Automation of these scores could be utilized to enhance triage and resource allocation during the ultra-early phase of resuscitation.

Assessing Patients Perception: Analyzing the Quality, Reliability, Comprehensibility, and the Mentioned Medical Concepts of Traumatic Brain Injury Videos on YouTube.

OBJECTIVE: The aim of this study is to analyze the quality, reliability, comprehensibility, and the mentioned medical concepts of traumatic brain injury (TBI) related videos on YouTube. METHODS: "Traumatic brain injury (TBI)" and Google Trends related queries were used, and 328 videos were included. Categorization, quality assessment via a 5-point Global Quality Scale, reliability evaluation using a modified DISCERN scale, comprehensibility assessment using Flesch-Kincaid Reading Ease and Flesch-Kincaid Grade Level, and consensus-based classification for usefulness were performed. Target audience categorization was content-based. Text mining techniques were used to identify biomedical terms in video transcripts. RESULTS: Most of the videos were intended for nonhealthcare professionals and deemed useful. The videos had intermediate quality and a moderate level of reliability. The comprehensibility of the videos exceeded the recommended levels. Videos predominantly covered TBI symptoms, the severity of the condition, its impact on individuals, and possibly strategies related to diagnosis and treatment. CONCLUSIONS: Results suggest the requirement for higher quality and reliability in YouTube content about TBI. Emphasis should be placed on clear and accurate language to promote comprehensibility. Continued research, guidelines, education, and platform oversight can enhance the spread of reliable health information on social media, benefiting creators, and consumers in this field.

Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms.

OBJECTIVE: This study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injury (TBI). METHODS: A single-center prospectively collected cohort of neurosurgical intensive care unit admissions was analyzed. We extracted ICP-related data within the first 6 hours and processed them using complex algorithms. To indicate TBI severity and short-term prognosis, Glasgow Coma Scale score on the first postoperative day and Glasgow Outcome Scale-Extended score at discharge were used as binary outcome variables. A univariate logistic regression model was developed to predict TBI severity using only mean ICP values. Subsequently, 3 multivariate Random Forest (RF) models were constructed using different combinations of mean and complexity metrics of ICP-related data. To avoid overfitting, five-fold cross-validations were performed. Finally, the best-performing multivariate RF model was used to predict patients' discharge Glasgow Outcome Scale-Extended score. RESULTS: The logistic regression model exhibited limited predictive ability with an area under the curve (AUC) of 0.558. Among multivariate models, the RF model, combining the mean and complexity metrics of ICP-related data, achieved the most robust ability with an AUC of 0.815. Finally, in terms of predicting discharge Glasgow Outcome Scale-Extended score, this model had a consistent performance with an AUC of 0.822. Cross-validation analysis confirmed the performance. CONCLUSIONS: This study demonstrates the clinical utility of the RF model, which integrates the mean and complexity metrics of ICP data, in accurately predicting the TBI severity and short-term prognosis.

Confounding factors impacting the Glasgow coma score: a literature review.

BACKGROUND: The Glasgow coma score (GCS) is a clinical tool used to measure level of consciousness in traumatic brain injury and other settings. Despite its widespread use, there are many inaccuracies in its reporting. One source of inaccuracy is confounding factors which affect consciousness as well as each sub-score of the GCS. The purpose of this article was to create a comprehensive list of confounding factors in order to improve the accuracy of the GCS and ultimately improve decision-making. METHODS: An English language literature search was conducted discussing GCS and multiple other keywords. Ultimately, 64 out of 3972 articles were included for further analysis. RESULTS: A multitude of confounding factors were identified which may affect consciousness or GCS sub-scores including the eye exam, motor exam and the verbal response. CONCLUSIONS: An up-to-date comprehensive list of confounding factors has been created that may be used to aide in GCS recording in hopes of improving its accuracy and utility.

Hsa_circ_0018401 and miR-127-5p Expressions Are Diagnostic and Prognostic Markers for Traumatic Brain Injury (TBI) in Trauma Patients.

This study investigated the potentials of hsa_circ_0018401 and miR-127-5p in traumatic brain injury (TBI) diagnosis, stratification and outcome prediction. A retrospective analysis of clinical data and blood samples of n = 109 TBI patients was performed. Expression levels of hsa_circ_0018401 and miR-127-5p were measured using Real-time PCR. The diagnostic values, as well as the values in TBI stratification, of hsa_circ_0018401 and miR-127-5p were assessed by receiver operating characteristic analyses. The prognostic impacts were investigated for one-year endpoint events using multivariable Cox regression analyses and receiver operating characteristic analysis. The target genes for miR-127-5p were predicted. An upregulation of hsa_circ_0018401 and a downregulation of miR-127-5p expression was detected in patients with TBI, and the highest or lowest levels were found in moderate/severe TBI. A negative correlation between miR-423-3p level and Dual luciferase reporter assay verified the binding relationship between hsa_circ_0018401 and miR-127-5p. Hsa_circ_0018401 and miR-127-5p, used alone or combinedly, showed clinical values for TBI diagnosis and stratification, as well as outcome prediction. The proteins for target genes covered TBI-related functions and pathways. Therefore, hsa_circ_0018401 and miR-127-5p could represent promising new biomarkers to identify TBI from healthy, moderate/severe TBI from mild TBI, as well as to predict the TBI outcome.

Prevalence of fatigue and cognitive impairment after traumatic brain injury.

BACKGROUND: Following traumatic brain injury (TBI) some patients develop lingering comorbid symptoms of fatigue and cognitive impairment. The mild cognitive impairment self-reported by patients is often not detected with neurocognitive tests making it difficult to determine how common and severe these symptoms are in individuals with a history of TBI. This study was conducted to determine the relative prevalence of fatigue and cognitive impairment in individuals with a history of TBI. METHODS: The Fatigue and Altered Cognition Scale (FACs) digital questionnaire was used to assess self-reported fatigue and cognitive impairment. Adults aged 18-70 were digitally recruited for the online anonymous study. Eligible participants provided online consent, demographic data, information about lifetime TBI history, and completed the 20 item FACs questionnaire. RESULTS: A total of 519 qualifying participants completed the online digital study which included 204 participants with a history of TBI of varied cause and severity and 315 with no history of TBI. FACs Total Score was significantly higher in the TBI group (57.7 ± 22.2) compared to non-TBI (39.5 ± 23.9; p<0.0001) indicating more fatigue and cognitive impairment. When stratified by TBI severity, FACs score was significantly higher for all severity including mild (53.9 ± 21.9, p<0.0001), moderate (54.8 ± 24.4, p<0.0001), and severe (59.7 ± 20.9, p<0.0001) TBI. Correlation analysis indicated that more severe TBI was associated with greater symptom severity (p<0.0001, r = 0.3165). Ancillary analysis also suggested that FACs scores may be elevated in participants with prior COVID-19 infection but no history of TBI. CONCLUSIONS: Adults with a history of even mild TBI report significantly greater fatigue and cognitive impairment than those with no history of TBI, and symptoms are more profound with greater TBI severity.

Identification of novel blood-based extracellular vesicles biomarker candidates with potential specificity for traumatic brain injury in polytrauma patients.

Objective: The goal of this study was to identify changes in extracellular vesicles (EV) surface proteins specific to traumatic brain injury (TBI), which could be used as a diagnostic and prognostic tool in polytrauma patients. Summary Background Data: Known serum TBI-specific biomarkers (S100B, NSE, and GFAP), which can predict the severity and outcome of isolated TBI, lose their predictive value in the presence of additional extracranial injuries. Extracellular vesicles (EVs) are released from cells in response to various stimuli and carry specific cargo/surface molecules that could be used for tracking injury-responding cells. Methods: EVs were isolated using size exclusion chromatography (SEC) from the plasma of two groups of patients (with isolated TBI, ISS≥16, AIShead≥4, n=10; and polytraumatized patients without TBI ISS≥16, AIShead=0, n=10) collected in the emergency room and 48 h after trauma. EVs' surface epitope expression was investigated using a neurospecific multiplex flow cytometry assay and compared with healthy controls (n=10). Three enrichments of EV epitopes found to be specific to TBI were validated by western blot. Results: The expression of 10 EV epitopes differed significantly among the patient and control groups, and five of these epitopes (CD13, CD196, MOG, CD133, and MBP) were TBI-specific. The increased expression of CD196, CD13, and MOG-positive EVs was validated by western blot. Conclusion: Our data showed that TBI is characterized by a significant increase of CD13, CD196, MOG, CD133, and MBP-positive EVs in patients' plasma. A high level of MOG-positive EVs negatively correlated with the Glasgow Coma Scale score at admission and could be an indicator of poor neurological status.

Reliability in distinguishing murder from accidental event with current forensic criteria in traumatic head injury.

Discovering a body displaying signs of multiple head trauma requires a thorough examination by the forensic pathologist, and a multidisciplinary approach is recommended. However, determining the manner of death is not always possible. We present a case in which the body of a 60-year-old man was discovered lying face down on the floor of his apartment, partially unclothed, surrounded by blood and vomit, and presenting numerous head injuries. The autopsy concluded that the cause of death was a result of post-traumatic brain injury. Nevertheless, applying current criteria made it challenging to ascertain whether the trauma stemmed from an accidental event or an assault.