Site-Specific Increases in Utero- and Fetoplacental Arterial Vascular Resistance in eNOS-Deficient Mice Due to Impaired Arterial Enlargement1
Bibliographic record
Abstract
The sites of elevated vascular resistance that impede placental perfusion in pathological pregnancies are unknown. In the current study, we identified these sites in a knockout mouse model (eNOS(-/-)) with reduced uterine (-55%) and umbilical (-29%) artery blood flows caused by endothelial nitric oxide synthase deficiency. Uteroplacental and fetoplacental arterial vascular trees of pregnant mice near term were imaged using x-ray microcomputed tomography (n = 5-10 placentas from 3-5 dams/group). The resulting three-dimensional images were analyzed to assess vessel geometry and vascular resistance. In control and eNOS(-/-) trees, ∼90% of total uteroplacental vascular resistance was located in the radial arteries. Changes in eNOS(-/-) vessel geometry, including 30% reductions in uterine, radial, and spiral artery diameters, were calculated to increase arterial resistance downstream of the uterine artery by 2.3-fold, predicting a 57% decrease in uterine blood flow. Despite large reductions in eNOS(-/-) spiral arteries (-55% by volume) and maternal canals (-67% by volume), these vessels were relatively minor contributors to resistance. In the eNOS(-/-) fetoplacental tree, the number of arterioles (50-75 μm diameter) increased by 26%. Nevertheless, calculated resistance rose by 19%, predominantly because arteries near the periphery of the tree selectively exhibited a 7%-9% diameter reduction. We conclude that previously observed decreases in uterine and umbilical blood flows in eNOS(-/-) pregnancies are associated with markedly divergent structural changes in the uteroplacental versus fetoplacental circulations. Results showed the radial arteries were critical determinants of uteroplacental resistance in mice and therefore warrant greater attention in future studies in pathological human pregnancies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".