Concussive injury elicits JNK‐mediated human astrocyte retraction (651.10)
Bibliographic record
Abstract
Background: The pathology of traumatic brain injury (TBI) is poorly understood, but likely involves blood‐brain barrier (BBB) dysfunction. The BBB is lined with microvascular endothelial cells, ensheathed by astrocyte endfeet. We investigated human astrocytes and endothelial cell responses to concussive injury in vitro. Methods: Concussive injury in vitro was elicited by using a Cell Injury Controller II, a device which induces regulated and repeatable pulse of gas pressure to deform a flexible membrane. Human astrocytes (HA) or human immortalized cerebral microvascular endothelial cells (hCMEC/D3) grown on flexible culture membranes were subjected to a single (20, 35, or 55% deformation) or repeated (20% deformation; 6‐consecutive injuries spanned every 2 minutes) concussive injuries. Cells were assayed for morphological changes (retraction), detachment/cell death, and MAPK activation (phosphorylation of JNK1/2, ERK1/2, and p38). Results : Significant HA retraction was elicited 30 min following either a single (55% deformation) or repeated concussive injury. HA retraction was not associated with cell death or detachment. HA recovered morphologically after 2 hrs post‐injury. Concussive injury increased phospho‐JNK1/2, but not phospho‐ERK1/2 or phospho‐p38. Pre‐treatment of HA with JNK inhibitors (SP600125 or SU3327) reduced injury‐induced HA retraction. In contrast, concussive injury failed to induce either morphological changes or MAPK activation in hCMEC/D3 Conclusion: Concussive injury induces JNK‐mediated astrocyte retraction in vitro , while sparing morphological changes in endothelial cells. In vivo , astrocyte retraction from endothelial cells may result in BBB dysfunction. JNK inhibition may be a potential therapeutic target for TBI. Grant Funding Source : Supported by the Children's Health Foundation (London ON) and HSFO Grant NA6914
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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) | 0.001 | 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 | 0.000 |
| 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".