Effect of low doses of cannabidiolic acid and ondansetron on <scp>LiCl</scp>‐induced conditioned gaping (a model of nausea‐induced behaviour) in rats
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To determine the minimally effective dose of cannabidiolic acid (CBDA) that effectively reduces lithium chloride (LiCl)-induced conditioned gaping reactions (nausea-induced behaviour) in rats and to determine if these low systemic doses of CBDA (5-0.1 μg·kg⁻¹) relative to those of CBD could potentiate the anti-nausea effects of the classic 5-hydroxytryptamine 3 (5-HT₃) receptor antagonist, ondansetron (OND). EXPERIMENTAL APPROACH: We investigated the efficacy of low doses of CBDA to suppress acute nausea, assessed by the establishment of conditioned gaping to a LiCl-paired flavour in rats. The potential of threshold and subthreshold doses of CBDA to enhance the reduction of nausea-induced conditioned gaping by OND were then determined. KEY RESULTS: CBDA (at doses as low as 0.5 μg·kg⁻¹) suppressed nausea-induced conditioned gaping to a flavour. A low dose of OND (1.0 μg·kg⁻¹) alone reduced nausea-induced conditioned gaping, but when it was combined with a subthreshold dose of CBDA (0.1 μg·kg⁻¹) there was an enhancement in the suppression of LiCl-induced conditioned gaping. CONCLUSIONS AND IMPLICATIONS: CBDA potently reduced conditioned gaping in rats, even at low doses and enhanced the anti-nausea effect of a low dose of OND. These findings suggest that combining low doses of CBDA and OND will more effectively treat acute nausea in chemotherapy patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".