Opioid receptor μ, not δ and κ, modulate food intake induced by ghrelin in laying chickens
Bibliographic record
Abstract
Evidence from animal studies suggests that the opioidergic system and ghrelin have a regulatory role in food intake, but their interaction(s) have not been studied in laying chickens. So in this study, four experiments (each included four groups) were designed. The first experiment was performed to evaluate the effect of ghrelin on the cumulative food intake. Experiments 2–4 were designed to investigate the possibility of μ, δ, or κ opioid receptors mediating ghrelin-induced hypophagia. All drugs were injected intracerebroventricularly (ICV) at 5 days of age. The results of this study showed that the ICV injection of 1.5 nmol ghrelin did not affect cumulative food intake. However, ICV injection of ghrelin with doses of 3 and 6 nmol significantly reduced the cumulative food intake ( p < 0.05). However, co-injection of ghrelin with naltrindole and norbinaltorphimine did not show a significant change in decreased food intake compared with ghrelin. Also, opioid μ receptor gene expression significantly increased ( p < 0.05), but δ and κ opioid receptors’ gene expression did not significantly change. These results indicated that the opioidergic system is involved in developing ghrelin-induced hypophagic effects in laying chickens. Accordingly, this effect of ghrelin to modify the nutritional behavior is possibly mediated by opioid μ receptor.
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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) | 0.001 | 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".