Effects of female sex hormones on expression of the Ang-(1–7)/Mas-R/nNOS pathways in rat brain
Bibliographic record
Abstract
Female sex hormones are considered to reduce the risk of ischemic stroke. As a part of the renin-angiotensin system, angiotensin-(1-7) [Ang-(1-7)] has recently been reported to play a role in protecting neuronal tissues from ischemic stroke. Thus, we examined the effects of female sex hormones on the levels of Ang-(1-7) and its downstream pathways in the brain. Female rats were ovariectomized and 17β-estradiol (17β-EST), progesterone (PGR), or a combination of 17β-EST plus PGR were administered. Our data demonstrated that lack of female sex hormones significantly decreased the levels of Ang-(1-7) in the cerebral cortex and hippocampal CA1 area. Also, we observed a linear relationship between cortex levels of Ang-(1-7) and plasma brain natriuretic peptide levels (as an indicator for risk of ischemic stroke). We further showed that lack of female sex hormones decreased the expression of Ang-(1-7), Mas-receptor (Mas-R), and neuronal nitric oxide synthase (nNOS). Overall, our findings show for the first time that Ang-(1-7) and Mas-R/nNOS in the cortex are influenced by circulating 17β-EST and (or) PGR, whereas Ang-(1-7) and its pathways in the hippocampal CA1 area are primarily altered by 17β-EST. This suggests that female sex hormones play a role in regulating the expression of Ang-(1-7) and its pathways during ischemic brain injuries.
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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) | 0.002 | 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".