Tetrahydrocannabinolic acid reduces nausea‐induced conditioned gaping in rats and vomiting in <i><scp>S</scp>uncus murinus</i>
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We evaluated the anti-emetic and anti-nausea properties of the acid precursor of Δ(9) -tetrahydrocannabinol (THC), tetrahydrocannabinolic acid (THCA), and determined its mechanism of action in these animal models. EXPERIMENTAL APPROACH: We investigated the effect of THCA on lithium chloride- (LiCl) induced conditioned gaping (nausea-induced behaviour) to a flavour, and context (a model of anticipatory nausea) in rats, and on LiCl-induced vomiting in Suncus murinus. Furthermore, we investigated THCA's ability to induce hypothermia and suppress locomotion [rodent tasks to assess cannabinoid1 (CB1 ) receptor agonist-like activity], and measured plasma and brain THCA and THC levels. We also determined whether THCA's effect could be blocked by pretreatment with SR141716 (SR, a CB1 receptor antagonist). KEY RESULTS: In rats, THCA (0.05 and/or 0.5 mg·kg(-1) ) suppressed LiCl-induced conditioned gaping to a flavour and context; the latter effect blocked by the CB1 receptor antagonist, SR, but not by the 5-hydroxytryptamine-1A receptor antagonist, WAY100635. In S. murinus, THCA (0.05 and 0.5 mg·kg(-1) ) reduced LiCl-induced vomiting, an effect that was reversed with SR. A comparatively low dose of THC (0.05 mg·kg(-1) ) did not suppress conditioned gaping to a LiCl-paired flavour or context. THCA did not induce hypothermia or reduce locomotion, indicating non-CB1 agonist-like effects. THCA, but not THC was detected in plasma samples. CONCLUSIONS AND IMPLICATIONS: THCA potently reduced conditioned gaping in rats and vomiting in S. murinus, effects that were blocked by SR. These data suggest that THCA may be a more potent alternative to THC in the treatment of nausea and vomiting.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".