Regulating hippocampal hyperexcitability through<scp>GABAB</scp>Receptors
Bibliographic record
Abstract
Abstract Disturbances of GABAergic inhibition are a major cause of epileptic seizures. GABA exerts its actions via ionotropic GABAA receptors and metabotropic G protein-coupled GABAB receptors. Malfunction of GABAA inhibition has long been recognized in seizure genesis but the role of GABAB receptors in controlling seizure activity is still not well understood. Here, we examined the anticonvulsive, or inhibitory effects, of GABAB receptors in a mouse model of hippocampal kindling as well as mouse hippocampal slices through the use of GS 39783, a positive allosteric GABAB receptor modulator, and CGP 55845, a selective GABAB receptor antagonist. When administered via intraperitoneal injections in kindled mice, GS 39783 (5 mg/kg) did not attenuate hippocampal EEG discharges, but did reduce aberrant hippocampal spikes, whereas CGP 55845 (10 mg/kg) prolonged hippocampal discharges and increased spike incidences. When examined in hippocampal slices, neither GS 39783 at 5 μmol/L nor the GABAB receptor agonist baclofen at 0.1 μmol/L alone significantly altered repetitive excitatory field potentials, but GS 39783 and baclofen together reversibly abolished these field potentials. In contrast, CGP 55845 at 1 μmol/L facilitated induction and incidence of these field potentials. In addition, CGP 55845 attenuated the paired pulse depression of CA3 population spikes and increased the frequency of EPSCs in individual CA3 pyramidal neurons. Collectively, these data suggest that GABABB receptors regulate hippocampal hyperexcitability by inhibiting CA3 glutamatergic synapses. We postulate that positive allosteric modulation of GABAB receptors may be effective in reducing seizure-related hyperexcitability.
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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".