Involvement of extracellular regulated kinase and p38 kinase in hippocampal seizure tolerance
Bibliographic record
Abstract
The mechanisms underlying brain seizure tolerance, a phenomenon in which brief periods of seizures protect brain against the lethal effects of subsequent sustained seizures, are poorly understood. Because brain seizure tolerance and brain ischemia tolerance likely share certain common mechanisms, the recent evidence that activation of extracellular regulated kinase (ERK) and p38 kinase pathways plays a critical role in ischemic preconditioning suggests that a similar mechanism may underlie brain seizure tolerance. We investigated the hypothesis in a rat kainic acid preparation of seizure preconditioning and tolerance, which was established by induction of one episode of priming epileptic status lasting for 20 min on the first day and another episode of sustained epileptic status lasting for 2 hr on the second day. We observed that acute seizures lead to a rapid activation of ERK and p38 in the hippocampal CA3 area, the brain region most susceptible to the lethal effects of epileptic status. Pretreatment with the ERK inhibitor PD98059 and the p38 inhibitor SB203580 selectively reduces seizure-elicited activation of ERK and p38, respectively, and significantly reduces priming seizure-induced protection of CA3 neurons. These findings indicate that, similar to brain ischemia tolerance, brain seizure tolerance also involves the ERK and p38 signaling pathways.
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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) | 0.001 | 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".