Cannabidiol Exerts Anticonvulsant Effects Alone and in Combination with Δ9-THC through the 5-HT1A Receptor in the Neocortex of Mice
Bibliographic record
Abstract
Cannabinoids have shown potential in drug-resistant epilepsy treatment; however, we lack knowledge on which cannabinoid(s) to use, dosing, and their pharmacological targets. This study investigated (i) the anticonvulsant effect of Cannabidiol (CBD) alone and (ii) in combination with Delta-9 Tetrahydrocannabinol (Δ9-THC), as well as (iii) the serotonin (5-HT)1A receptor’s role in CBD’s mechanism of action. Seizure activity, induced by 4-aminopyridine, was measured by extracellular field recordings in cortex layer 2/3 of mouse brain slices. The anticonvulsant effect of 10, 30, and 100 µM CBD alone and combined with Δ9-THC was evaluated. To examine CBD’s mechanism of action, slices were pre-treated with a 5-HT1A receptor antagonist before CBD’s effect was evaluated. An amount of ≥30 µM CBD alone exerted significant anticonvulsant effects while 10 µM CBD did not. However, 10 µM CBD combined with low-dose Δ9-THC (20:3 ratio) displayed significantly greater anticonvulsant effects than either phytocannabinoid alone. Furthermore, blocking 5-HT1A receptors before CBD application significantly abolished CBD’s effects. Thus, our results demonstrate the efficacy of low-dose CBD and Δ9-THC combined and that CBD exerts its effects, at least in part, through 5-HT1A receptors. These results could address drug-resistance while providing insight into CBD’s mechanism of action, laying the groundwork for further testing of cannabinoids as anticonvulsants.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".