The impacts of endogenous progesterone and exogenous progestin on vascular endothelial cell, and smooth muscle cell function: A narrative review
Bibliographic record
Abstract
Vascular endothelial and smooth muscle cell dysfunction proceed the development of numerous vascular diseases, such as atherosclerosis. Both estrogen and progesterone receptors are present on vascular endothelial and smooth muscle cells, and therefore it has been postulated that these compounds may affect vascular function. It has been well-established that estrogen is a vasoprotective compound, however, the effects of progesterone on vascular function are not well understood. This narrative review summarizes the current research investigating the impact of both endogenous progesterone, and exogenous synthetic progestin on vascular endothelial and smooth muscle cell function and identifies discrepancies on their effects in vitro and in vivo. We speculate that an inverted-U dose response curve may exist between nitric oxide bioavailability and progesterone concentration, and that the androgenic properties of a progestin may influence vascular function. Future research is needed to discern the effects of both endogenous progesterone and exogenous progestin on vascular endothelial and smooth muscle cell function with consideration for the impacts of progesterone/progestin dose, and progestin type.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".