Food intake regulation in rodents: Y<sub>5</sub>or Y<sub>1</sub>NPY receptors or both?
Bibliographic record
Abstract
Neuropeptide Y (NPY), one of the most abundant peptides in rat and human brains, appears to act in the hypothalamus to stimulate feeding. It was first suggested that the NPY Y1receptor (Y1R) was involved in feeding stimulated by NPY. More recently a novel NPY receptor subtype (Y5R) was identified in rat and human as the NPY feeding receptor subtype. There is, however, no absolute consensus since selective Y1R antagonists also antagonize NPY-induced hyperphagia. Nevertheless, new anti-obesity drugs may emerge from further pharmacological characterization of the NPY receptors and their antagonists. A large panel of Y1R and Y5R antagonists (such as CGP71683A, BIBO3304, BIBP3226, 1229U91, and SYNAPTIC and BANYU derivatives but also patentable in-house-synthesized compounds) have been evaluated through in vitro and in vivo tests in an attempt to establish a predictive relationship between the binding selectivity for human receptors, the potency in isolated organs assays, and the inhibitory effect on food intake in both normal and obese hyperphagic rodents. Although these results do not allow one to conclude on the implication of a single receptor subtype at the molecular level, this approach is crucial for the design of novel NPY receptor antagonists with potential use as anti-obesity drugs and for evaluation of their possible adverse peripheral side effects, such as hypotension.Key words: obesity, weight reduction, food intake, neuropeptide Y, rodents.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".