Serotonin Depletion Attenuates AY‐9944–Mediated Atypical Absence Seizures
Bibliographic record
Abstract
PURPOSE: To test the hypothesis that serotonin (5-HT) plays a role in the modulation of experimental atypical absence seizures. METHODS: Male Long-Evans hooded rats were treated from postnatal day (P) 2 to P20 with the cholesterol inhibitor AY-9944 (AY). Epidural electrodes were implanted for electrocorticography (ECoG) followed by serotonin depletion by using para-cholorophenylalanine (PCPA). High-performance liquid chromatography (HPLC) was used to measure the levels of serotonin and its metabolite (5-HIAA) in various brain regions. Serotonin metabolism was computed by using the 5-HIAA/5-HT ratio and used to ascertain differences between groups. RESULTS: PCPA treatment was associated with a significant decrease in the total slow spike-and-wave discharge (SSWD) duration in AY-treated rats compared with controls (p < 0.01). HPLC data confirmed the PCPA depletion of 5-HT and 5-HIAA in cortex, thalamus, hippocampus, and brainstem compared with naïve rats. AY-treated rats showed higher levels of 5-HIAA and 5-HT in the same brain regions, with a concomitant decrease in rates of serotonin turnover. CONCLUSIONS: The data indicate that serotonin depletion protects against experimental atypical absence seizures. The increased levels of 5-HIAA and 5-HT and altered rates of serotonin turnover suggest that the serotonergic neurotransmission may be perturbed in the AY rat.
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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 | 0.001 |
| 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".