Spreading Depression Expands Traumatic Injury in Neocortical Brain Slices
Bibliographic record
Abstract
Traumatic brain injury (TBI) is particularly common in young people, generating healthcare costs that can span decades. The cellular processes activated in the first minutes following injury are poorly understood, and the 3-4 h following trauma are crucial for reducing subsequent injury. Spreading depression (SD) is a profound inactivation of neurons and glia lasting 1-2 min that arises focally and migrates outward across gray matter. In the hours following focal stroke, the metabolic stress of energy reduction and recurring SD-like events (peri-infarct depolarizations, PIDs) interact to promote neuronal injury. Similar recurring depolarizations might evolve immediately following TBI and exacerbate neuronal damage peripheral to the impact site. To test this possibility and examine if certain drugs might limit damage by inhibiting what we term traumatic spreading depression (tSD), we developed a technique whereby a small weight was dropped onto a live slice of rat neocortex while imaging changes in light transmittance (LT). Imaging revealed a propagating front of increased LT arising at the border of the impact site. Traumatic SD significantly expanded the region of ensuing damage. Both tSD and subsequent damage were blocked by the NMDA receptor antagonist MK-801 (100 microM) or the sigma-1 receptor (sigma1R) ligands dextromethorphan (30 microM) or BD-1063 (100 microM). Co-application of the sigma1R antagonist (+)3-PPP with DM reversed the block as did lowering temperature from 35 degrees C to 32 degrees C. This study provides evidence that an event similar to peri-infarct depolarization can arise from an injury site in neocortex within seconds following impact and act to expand the region of acute neuronal damage.
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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 | 0.001 | 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".