Pregnancy-Induced Alterations of Vascular Function in Mouse Mesenteric and Uterine Arteries1
Bibliographic record
Abstract
Normal pregnancy involves dramatic changes to maternal vascular function, while abnormal vascular adaptations may contribute to pregnancy-associated diseases such as preeclampsia. Many genetic mouse models have recently emerged to study vascular pathologies of pregnancy. However, vascular adaptations to pregnancy in normal mice are not fully understood. Thus, we studied changes in vascular reactivity during normal mouse pregnancy. We hypothesized that pregnant mice will have enhanced endothelial-dependent vasodilation compared with nonpregnant mice, via an enhancement of the nitric oxide synthase (NOS) prostaglandin H synthase (PGHS), and other endothelial-derived hyperpolarizing pathways. Late pregnant (Day 17-18) C57BL/6J mice (n = 10) were compared with nonpregnant mice (n = 7). Uterine and mesenteric arteries were mounted on a wire myograph system and assessed for endothelium-dependent (methacholine) and -independent (sodium nitroprusside; SNP) relaxation responses. Endothelial-dependent relaxation was enhanced in pregnant uterine and mesenteric arteries, which was blunted after the addition of inhibitors of the PGHS or NOS pathways. In nonpregnant mice, these pathways had no effect in modulating relaxation in uterine arteries, whereas vasodilation in mesenteric arteries was reduced only by NOS inhibition. Both uterine and mesenteric vessels had nonnitric oxide- and nonprostaglandin-mediated relaxation, but this relaxation was not enhanced during pregnancy. Endothelial-independent relaxation was also enhanced in pregnant uterine but not mesenteric arteries. Our data indicate that uterine and mesenteric arteries from pregnant mice have enhanced vasodilation. Understanding vascular adaptations to normal mouse pregnancy is crucial for interpreting changes that may occur in genetic mouse models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".