Postnatal epigenetic influences on seizure susceptibility in seizure-prone versus seizure-resistant rat strains.
Bibliographic record
Abstract
The creation of seizure-prone (Fast) and seizure-resistant (Slow) rat strains via selective breeding implies genetic control of relative seizure vulnerability, yet ample data also advocates an environmental contribution. To investigate potential environmental underpinnings to the differential seizure sensitivities in these strains, the authors compared amygdala kindling profiles in adult male Fast and Slow rats raised by (a) their own mother, (b) a foster mother from the same strain, or (c) a foster mother from the opposing strain. Ultimately, strain-specific kindling profiles were not normalized by cross-fostering. Instead, both strains became more seizure-prone regardless of maternal affiliation (i.e., cross-fostered groups from both strains kindled faster than uncrossed controls). Interhemispheric seizure spread was also facilitated in cross-fostered Slow rat groups and was associated with increased commissural cross-sectional areas, giving them a Fast-like profile. It is important to note, however, that all Fast groups remained significantly more seizure-prone than Slow groups, suggesting that although the postnatal environment strongly influenced seizure disposition in both strains, it did not wholly account for their relative dispositions. Investigation into mechanisms fundamental to cross-fostering-induced seizure facilitation should help prevent postnatal worsening of pathology in already seizure-prone individuals.
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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".