Concussive injury elicits human cerebrovascular endothelial cell activation in vitro
Bibliographic record
Abstract
Concussive brain injury may directly injure cerebrovascular endothelial cells (CVEC) of the blood‐brain barrier and/or CVEC may be altered by circulating trauma‐induced inflammatory mediators. Thus, we assessed for activation of CVEC by either a concussive injury or by application of plasma from trauma patients. Methods Plasma was obtained from pediatric multisystem trauma patients (injury severity score≥12) and age/sex‐matched controls. Human immortalized CVEC (hCMEC/D3; provided by Dr. P. Couraud, INSERM) were employed to assess for activation in vitro . Concussive injury was induced in hCMEC/D3 grown on flexible supports with a compressed air pulse at 4 psi/well. In parallel, uninjured CVEC were treated with 20% v/v blood plasma collected from trauma patients or healthy controls. Results A single concussive injury to hCMEC/D3 resulted in increased nitric oxide (NO; DAF‐FM nitrosation) at 15 and 30 minutes, increased reactive oxygen species (ROS; DHR‐123 oxidation) at 2 hours and increased 51 Cr‐PMN adhesion at 6 hours. In contrast, application of 20% v/v trauma patient plasma to hCMEC/D3 failed to induce oxidative stress, activate NF‐κB (ELISA) or elicit PMN adhesion assessed under “flow” (relative to control plasma‐treated hCMEC/D3). Conclusions Direct concussive injury induces hCMEC/D3 activation in vitro , whereas application of 20% v/v trauma plasma is not potent enough to activate CVEC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".