Effect of Multiparity on Vascular Compliance and Collagen Content
Bibliographic record
Abstract
We have shown that vascular compliance is decreased in multiparous rats. When the balance of collagen synthesis/breakdown changes, the functional characteristics of the vessels are altered. Collagen degradation is regulated by matrix metalloproteinases (MMPs). We have previously shown that multiparity increases oxidative stress and peroxynitrite formation. Peroxynitrite can change MMP levels in vascular tissues and thus alter collagen content. We examined the effect of multiparity on collagen content of aorta and mesenteric arteries by Trichrome Masson staining of paraffin imbedded vascular sections, by total vascular collagen and by hydroxyproline. MMP2 activity in vascular tissues was measured by gelatin zymography. Functional changes in vascular compliance were assessed by pressure myography. Data demonstrate that multiparity is associated with decreased MMP2 activity in aorta (870±229 vs 4797±1116) and mesenteric arteries (544±113 vs 2294±777). This was accompanied by increased total collagen (aorta: 536±37 vs 259±32; mesentery: 426±24 vs 206±12) and hydroxyproline (aorta: 149.5±3 vs 64±7; mesentery: 142.8±12.8 vs 74.0±11). Vascular compliance was decreased in vessels from multiparous rats. These data suggest that multiparity decreases vascular compliance via mechanisms involving MMP‐regulated collagen degradation which is downstream of the peroxynitrite signalling pathway.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".