Amygdala amino acid and monoamine levels in genetically Fast and Slow kindling rat strains during massed amygdala kindling: a microdialysis study
Bibliographic record
Abstract
We investigated the neurochemistry of epileptic seizures in rats selectively bred to be seizure-prone (Fast) vs. seizure-resistant (Slow) to amygdala kindling. Microdialysis was used to measure levels of amino acids [glutamate, aspartate and gamma-aminobutyric acid (GABA)] and monoamines (noradrenaline, dopamine and serotonin) during 'massed' stimulation (MS) (every 6 min) of the ipsilateral amygdala for a total of 40 stimulation trials. Behavioral seizure profiles together with their afterdischarge thresholds (ADTs) and associated durations were assessed during the procedure, and subsequently were redetermined 1, 7 and 14 days later. Then normal 'daily' kindling commenced and continued until the animal reached the fully kindled state. During MS, several generalized seizures were triggered in Fast rats that were associated with long afterdischarge (AD) durations and intermittent periods of elevated thresholds, but in Slow rats, most stimulations were associated with stable ADTs and short ADs. Progressively increasing extracellular glutamate and decreasing GABA was observed in Fast rats during the MS, whereas Slow rats showed levels similar to baseline values. Levels of noradrenaline and dopamine, but not of serotonin, were also increased in both strains throughout the MS treatment. In Fast rats, a dramatic lengthening of AD durations occurred 7 and 14 days following MS, as well as subsequent strong positive transfer to daily kindling, all of which were not seen in Slow rats. Together, these results show that repeated, closely spaced stimulations of the amygdala can differentially alter excitatory and/or inhibitory transmitter levels in a seizure network, and that sensitivity to this manipulation is genetically determined.
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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 | 0.001 | 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 | 0.001 |
| 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".