<i>In vitro</i>and non‐invasive<i>in vivo</i>effects of the cannabinoid‐1 receptor agonist<scp>AM</scp>841 on gastrointestinal motor function in the rat
Bibliographic record
Abstract
BACKGROUND: Cannabinoids have been traditionally used for the treatment of gastrointestinal (GI) symptoms, but the associated central effects, through cannabinoid-1 receptors (CB1R), constitute an important drawback. Our aims were to characterize the effects of the recently developed highly potent long-acting megagonist AM841 on GI motor function and to determine its central effects in rats. METHODS: Male Wistar rats were used for in vitro and in vivo studies. The effect of AM841 was tested on electrically induced twitch contractions of GI preparations (in vitro) and on GI motility measured radiographically after contrast administration (in vivo). Central effects of AM841 were evaluated using the cannabinoid tetrad. The non-selective cannabinoid agonist WIN 55,212-2 (WIN) was used for comparison. The CB1R (AM251) and CB2R (AM630) antagonists were used to characterize cannabinoid receptor-mediated effects of AM841. KEY RESULTS: AM841 dose-dependently reduced in vitro contractile activity of rat GI preparations via CB1R, but not CB2R or opioid receptors. In vivo, AM841 acutely and potently reduced gastric emptying and intestinal transit in a dose-dependent and AM251-sensitive manner. The in vivo GI effects of AM841 at 0.1 mg/kg were comparable to those induced by WIN at 5 mg/kg. However, at this dose, AM841 did not induce any sign of the cannabinoid tetrad, whereas WIN induced significant central effects. CONCLUSIONS & INFERENCES: The CB1R megagonist AM841 may potently depress GI motor function in the absence of central effects. This effect may be mediated peripherally and may be useful in the treatment of GI motility disorders.
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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 | 0.000 |
| 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".