Effect of nitric oxide synthase inhibition on cardiovascular and hormonal regulation during pregnancy in the rat
Bibliographic record
Abstract
Recent studies have shown that nitric oxide (NO) biosynthesis increases in pregnancy and that inhibition of nitric oxide synthase (NOS) induces some pathological processes characteristic of preeclampsia. The current project sought to study the effect of the NOS inhibitor Nω-nitro-L-arginine methyl ester (L-NAME, 10 µg·min-1, sc for 7 days) on plasma volume, plasma atrial natriuretic factor (ANF), plasma endothelin-1 (ET), and plasma renin activity (PRA) during gestation in conscious rats. NOS inhibition caused mean arterial pressure to increase in both virgin and 21-day pregnant rats. Plasma volume fell in the pregnant rats [L-NAME, 4.5 ± 0.3 mL·100 g-1 body wt. (n = 7) vs. D-NAME, 6.8 ± 0.2 mL·100 g-1 body wt. (n = 10); P < 0.05] but not in the virgin rats [L-NAME, 4.3 ± 0.1 mL·100 g-1 body wt. (n = 6) vs. D-NAME, 4.8 ± 0.2 mL·100 g-1 body wt. (n = 8)]. There was no effect of NOS inhibition on plasma ANF levels or PRA in either the virgin or pregnant rats. However, L-NAME did decrease plasma ET levels in the pregnant rats [L-NAME, 19.6 ± 1.6 pg·mL-1 (n = 8) vs. D-NAME, 11.6 ± 2.5 pg·mL-1 (n = 9); P < 0.05]. Our results confirm that NO is involved in cardiovascular homeostasis in pregnancy; NOS inhibition selectively reduces plasma volume in pregnant rats, thus mimicking a major pathophysiological perturbation of preeclampsia. However, it does not induce the hormonal changes characteristic of preeclampsia, namely the decrease in PRA and increase in plasma ET and ANF levels. Key words: plasma volume, preeclampsia, endothelin, atrial natriuretic factor, plasma renin activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".