Arotinolol is a weak partial agonist on β<sub>3</sub>-adrenergic receptors in brown adipocytes
Bibliographic record
Abstract
Arotinolol, a clinically used α/β-adrenergic blocker, has been demonstrated to be an anti-obesity agent. The anti-obesity effect of arotinolol was suggested to be the result of direct activation of thermogenesis in brown-fat cells. We tested the ability of arotinolol to stimulate thermogenesis (oxygen consumption) in isolated brown-fat cells and in intact animals. Arotinolol stimulated thermogenesis in brown-fat cells isolated from mouse and hamster. A relatively low sensitivity to the β-adrenergic antagonist propranolol (pKB[Formula: see text] 6) indicated that arotinolol interacted with the β3-adrenergic receptor. On the β3-receptor, arotinolol was a very weak (EC50[Formula: see text] 20 µM) and only partial ([Formula: see text]50 %) agonist, but arotinolol also demonstrated the properties of being a β3-receptor antagonist with a pKBof 5.7. In intact animals, only the antagonistic action of arotinolol could be observed. Because arotinolol is only a very weak and partial agonist on the β3-receptors, direct stimulation of thermogenesis in brown adipose tissue is unlikely to be sufficient to cause significant weight loss. It may be necessary to invoke additional pathways to explain the anti-obesity effects of chronic treatment with arotinolol.Key words: arotinolol, β3-adrenergic receptor, brown adipose tissue, thermogenesis, mouse, hamster, rat.
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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 | 0.000 |
| 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".