Cannabidiolic acid prevents vomiting in <i><scp>S</scp>uncus murinus</i> and nausea‐induced behaviour in rats by enhancing 5‐<scp>HT<sub>1A</sub></scp> receptor activation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To evaluate the ability of cannabidiolic acid (CBDA) to reduce nausea and vomiting and enhance 5-HT(1A) receptor activation in animal models. EXPERIMENTAL APPROACH: We investigated the effect of CBDA on (i) lithium chloride (LiCl)-induced conditioned gaping to a flavour (nausea-induced behaviour) or a context (model of anticipatory nausea) in rats; (ii) saccharin palatability in rats; (iii) motion-, LiCl- or cisplatin-induced vomiting in house musk shrews (Suncus murinus); and (iv) rat brainstem 5-HT(1A) receptor activation by 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT) and mouse whole brain CB(1) receptor activation by CP55940, using [³⁵S]GTPγS-binding assays. KEY RESULTS: In shrews, CBDA (0.1 and/or 0.5 mg·kg⁻¹ i.p.) reduced toxin- and motion-induced vomiting, and increased the onset latency of the first motion-induced emetic episode. In rats, CBDA (0.01 and 0.1 mg·kg⁻¹ i.p.) suppressed LiCl- and context-induced conditioned gaping, effects that were blocked by the 5-HT(1A) receptor antagonist, WAY100635 (0.1 mg·kg⁻¹ i.p.), and, at 0.01 mg·kg⁻¹ i.p., enhanced saccharin palatability. CBDA-induced suppression of LiCl-induced conditioned gaping was unaffected by the CB₁ receptor antagonist, SR141716A (1 mg·kg⁻¹ i.p.). In vitro, CBDA (0.1-100 nM) increased the E(max) of 8-OH-DPAT. CONCLUSIONS AND IMPLICATIONS: Compared with cannabidiol, CBDA displays significantly greater potency at inhibiting vomiting in shrews and nausea in rats, and at enhancing 5-HT(1A) receptor activation, an action that accounts for its ability to attenuate conditioned gaping in rats. Consequently, CBDA shows promise as a treatment for nausea and vomiting, including anticipatory nausea for which no specific therapy is currently available.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".