Role of pericytes in the development of late-onset posttraumatic seizure.
Bibliographic record
Abstract
Traumatic brain injury (TBI) can cause the development of posttraumatic epilepsy (PTE) characterized by delayed onset. Increased convulsion risk persists for a long period, from a few months to several years after TBI. The late-onset PTE is often pharmacoresistant and occurs after an unpredictable latency. Thus, the latent period from TBI to the occurrence of the first unprovoked seizure may offer a window of opportunity for preventing the late-onset PTE. To clarify TBI pathology leading to the late-onset PTE, we observed changes of each cell type constituting neurovascular unit (NVU) in mice subjected to controlled cortical impact (CCI), which is an experimental traumatic brain injury at postoperative day 0-28. CCI mice showed that increased PDGFRβ expression in pericytes precedes increased Iba1 and GFAP expression in glial cells and neuronal hyperexcitability indicated by pilocarpine-induced convulsive behavior. Treatment with an inhibitor of PDGFRβ in the early phase after CCI suppressed microglial activation and neuronal hyperexcitability at postoperative day 28. Our results indicate that TBI-induced activation of pericytes characterized by increased PDGFRβ expression may drive the development of dysregulated NVU coordination including glial activation and neuronal hyperexcitability after TBI leading to the onset of PTE.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".