Mitral Cell β<sub>1</sub> and 5-HT<sub>2A</sub> Receptor Colocalization and cAMP Coregulation: A New Model of Norepinephrine-Induced Learning in the Olfactory Bulb
Bibliographic record
Abstract
In the present study we assess a new model for classical conditioning of odor preference learning in rat pups. In preference learning beta(1)-adrenoceptors activated by the locus coeruleus mediate the unconditioned stimulus, whereas olfactory nerve input mediates the conditioned stimulus, odor. Serotonin (5-HT) depletion prevents odor learning, with 5-HT(2A/2C) agonists correcting the deficit. Our new model proposes that the interaction of noradrenergic and serotonergic input with odor occurs in the mitral cells of the olfactory bulb through activation of cyclic adenosine monophosphate (cAMP). Here, using selective antibodies and immunofluorescence examined with confocal microscopy, we demonstrate that beta(1)-adrenoceptors and 5-HT(2A) receptors colocalize primarily on mitral cells. Using a cAMP assay and cAMP immunocytochemistry, we find that beta-adrenoceptor activation by isoproterenol, at learning-effective and higher doses, significantly increases bulbar cAMP, as does stroking. As predicted by our model, the cAMP increases are localized to mitral cells. 5-HT depletion of the olfactory bulb does not affect basal levels of cAMP but prevents isoproterenol-induced cAMP elevation. These results support the model. We suggest the mitral-cell cAMP cascade converges with a Ca(2+) pathway activated by odor to recruit CREB phosphorylation and memory-associated changes in the olfactory bulb. The dose-related increase in cAMP with isoproterenol implies a critical cAMP window because the highest dose of isoproterenol does not produce learning.
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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".