Investigation of the role of the NO–cGMP pathway on YC-1 and DEA/NO effects on thoracic aorta smooth muscle responses in a rat preeclampsia model
Bibliographic record
Abstract
The present study was designed to investigate the effects of YC-1, a nitric oxide (NO)-independent soluble guanylate cyclase (sGC) activator, and DEA/NO, a NO donor, on smooth muscle responses in the preeclampsia model with suramin-treated rats and on the levels of cyclic guanosine monophosphate (cGMP) of thoracic aorta rings isolated from term-pregnant rats. Rats of 2 groups, control group and suramin group, were given intraperitoneal injection of saline or suramin, respectively. Suramin injection caused increased blood pressure, protein in urine, and fetal growth retardation. Thoracic aorta rings were exposed to contractile and relaxant agents. KCl contraction and papaverine relaxation responses were similar. Relaxation responses of YC-1 and DEA/NO decreased in suramin group. In both groups in the presence of ODQ, a sGC inhibitor, the relaxation responses of YC-1 and DEA/NO decreased. The cGMP content was determined by radioimmunoassay technique. The content of cGMP in the suramin group decreased. In the presence of YC-1 and DEA/NO in both groups, cGMP content increased, but in ODQ-added groups, there was a significant decrease. We conclude that in preeclampsia, the decrease of relaxation responses and the decrease of cGMP content could be due to the reduction in stimulation of sGC and the decrease in cGMP levels.
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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) | 0.001 | 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".