Activation of the Adrenergic β-Receptor Stimulates Prolactin Release from Primary Cultured Pituitary Cells
Bibliographic record
Abstract
Prolactin release from primary cultured pituitary cells was investigated using a dynamic perifusion system. Although epinephrine (1 mumol/l) produced an elevation in the mean value of the prolactin concentration at the onset of the perifusion, the elevation was not statistically significant, and the overall effect of the epinephrine was to produce an inhibition of prolactin release. The alpha- and D2-receptors of the primary cultured cells were functionally removed by a pretreatment with phenoxybenzamine (0.1 or 1 mumol/l). Since phenoxybenzamine irreversibly inactivates both the adrenergic alpha- and the D2-receptors but does not block the beta-receptors, the enhanced stimulatory action of the epinephrine (1 mumol/l) on the phenoxybenzamine pretreated cells suggests the involvement of adrenergic beta-receptors in prolactin release. Perifusion of isoproterenol (1 mumol/l), a beta-receptor agonist, stimulated prolactin release. This stimulation was blocked by beta-receptor antagonism with propranolol (1 mumol/l) supporting the implication of the beta-receptor in prolactin release. In order to differentiate between beta 1- and beta 2-receptor involvement, we investigated the effects of ICI 118,551 (1 mumol/l) on isoproterenol-induced prolactin release. ICI 118,551, a selective beta 2-receptor antagonist, blocked the stimulatory action of isoproterenol on prolactin release indicating that the beta-receptor responsible for the stimulation of prolactin release belongs to the beta 2-receptor family. Moreover, the demonstration that salbutamol, a selective beta 2-receptor agonist, stimulated prolactin secretion offers further evidence in support of the role of the beta 2-receptor in the stimulation of prolactin release.(ABSTRACT TRUNCATED AT 250 WORDS)
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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 | 0.000 |
| 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".