Cannabidiol Interferes with Establishment of Δ <sup>9</sup> -Tetrahydrocannabinol-Induced Nausea Through a 5-HT <sub>1A</sub> Mechanism
Bibliographic record
Abstract
Introduction: Cannabinoid hyperemesis syndrome (CHS) is characterized by intense nausea and vomiting brought on by the use of high-dose Δ 9 -tetrahydrocannabinol (THC), the main psychotropic compound in cannabis. Cannabidiol (CBD), a nonpsychotropic compound found in cannabis, has been shown to interfere with some acute aversive effects of THC. In this study, we evaluated if CBD would interfere with THC-induced nausea through a 5-HT 1A receptor mechanism as it has been shown to interfere with nausea produced by lithium chloride (LiCl). Since CHS has been attributed to a dysregulated stress response, we also evaluated if CBD would interfere with THC-induced increase in corticosterone (CORT). Materials and Methods: The potential of CBD (5 mg/kg, ip) to suppress THC-induced conditioned gaping (a measure of nausea) was evaluated in rats, as well as the potential of the 5-HT 1A receptor antagonist, WAY-100635 (WAY; 0.1 mg/kg, ip), to reverse the suppression of THC-induced conditioned gaping by CBD. Last, the effect of CBD (5 mg/kg, ip) on THC-induced increase in serum CORT concentration was evaluated. Results: Pretreatment with CBD (5 mg/kg, ip) interfered with the establishment of THC-induced conditioned gaping ( p =0.007, relative to vehicle [VEH] pretreatment), and this was reversed by pretreatment with 0.1 mg/kg WAY. This dose of WAY had no effect on gaping on its own. THC (10 mg/kg, ip) significantly increased serum CORT compared with VEH-treated rats ( p =0.04). CBD (5 mg/kg, ip) pretreatment reversed the THC-induced increase in CORT. Conclusions: CBD attenuated THC-induced nausea as well as THC-induced elevation in CORT. The attenuation of THC-induced conditioned gaping by CBD was mediated by its action on 5-HT 1A receptors, similar to that of LiCl-induced nausea.
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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) | 0.002 | 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".