Vascular Effects of Progesterone
Bibliographic record
Abstract
-Vascular actions of progesterone have been reported, independently of estrogen, affecting both blood pressure and other aspects of the cardiovascular system. To study possible mechanisms underlying these effects, we examined the effects of P in vivo in intact rats and in vitro in isolated artery and vascular smooth muscle cell preparations. In anesthetized Sprague-Dawley rats, bolus intravenous injections of P (100 µg/kg) significantly decreased pressor responses to norepinephrine (0.3 µg/kg). In vitro, progesterone (10(-8) to 10(-5) mmol/L) produced a significant, dose-dependent relaxation of isolated helical strips, both of rat tail artery precontracted with KCl (60 mmol/L) or arginine vasopressin (3 nmol/L), and of rat aorta precontracted with KCl (60 mmol/L) or norepinephrine (0.1 µmol/L). In isolated vascular smooth muscle cells, progesterone (5x10(-)(7) mol/L) reversibly inhibited KCl (30 mmol/L) -induced elevation of cytosolic-free calcium by 64.1+/-5.5% (P:<0.05), and in whole-cell patch-clamp experiments, progesterone (5x10(-6) mol/L) reversibly and significantly blunted L-type calcium channel inward current, decreasing peak inward current to 65.7+/-4.3% of the control value (P:<0.05). Our results provide evidence that progesterone is a vasoactive hormone, inhibiting agonist-induced vasoconstriction. The data further suggest that progesterone effects on vascular tissue may, at least in part, be mediated by modulation of the L-type calcium channel current activity and, consequently, of cytosolic-free calcium content.
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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) | 0.003 | 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".