Progressive changes in hippocampal cytoarchitecture in a neurodevelopmental rat model of epilepsy: implications for understanding presymptomatic epileptogenesis, predictive diagnosis, and targeted treatments
Bibliographic record
Abstract
Epilepsies affect about 4% of the population and are frequently characterized by a prolonged "silent" period before the onset of spontaneous seizures. Most current animal models of epilepsy either involve acute seizure induction or kindling protocols that induce repetitive seizures. We have developed a rat model of epilepsy that is characterized by a slowly progressing series of behavioral abnormalities prior to the onset of behavioral seizures. In the current study, we further describe an accompanying progression of cytoarchitectural changes in the hippocampal formation. Groups of male and female SD rats received serial injections of a low dose of domoic acid (0.020 mg/kg) (or vehicle) throughout the second week of life. Postmortem hippocampal tissue was obtained on postnatal days 29, 64, and 90 and processed for glial fibrillary acidic protein (GFAP), NeuN, and calbindin expression. The data revealed no significant changes on postnatal day (PND) 29 but a significant increase in hilar NeuN-positive cells in some regions on PND 64 and 90 that were identified as ectopic granule cells. Further, an increase in GFAP positive cell counts and evidence of reactive astrogliosis was found on PND 90 but not at earlier time points. We conclude that changes in cellular expression, possibly due to on-going non-convulsive seizures, develop slowly in this model and may contribute to progressive brain dysfunction that culminates in a seizure-prone phenotype.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".