Reduced uterine perfusion pressure decreases functional capillary density in skeletal muscle
Bibliographic record
Abstract
The purpose of this study was to examine the functional and structural capillary density in the reduced uterine perfusion pressure (RUPP) model, which when performed during pregnancy is an established animal model of preeclampsia. We hypothesized that the RUPP model would be associated with capillary rarefaction and impaired capillary perfusion, which would be more pronounced in the pregnant state. Female Sprague-Dawley rats (n = 32) were randomized to nonpregnancy (Nonpregnant) or breeding (Pregnant) at 12 wk of age and again to RUPP or SHAM surgeries on gestational day (GD) 14 (or equivalent age in nonpregnant rats). On GD 20 (or equivalent), capillary structure and perfusion of the extensor digitorum longus were imaged using digital intravital video microscopy. Functional videos were analyzed by a blinded observer to measure capillary density, expressed as capillaries per millimeter intersecting three staggered reference lines (200 μm). Flow was scored as the percentage of capillaries having 1) continuous, 2) intermittent, or 3) stopped flow. Total capillary density was not different between groups. There was a main effect of RUPP surgery resulting in decreased continuous flow vessels (P < 0.01) and increased stopped flow (P < 0.01), which was driven by differences between pregnant animals (Continuous flow: pregnant SHAM 80.1 ± 7.8% vs. pregnant RUPP 67.8 ± 11.2%, P < 0.05) (Stopped flow: pregnant SHAM 8.7 ± 3.2% vs. pregnant RUPP 17.9 ± 5.7%, P < 0.01). Our results demonstrate that the RUPP surgery is associated with a decrease in functional capillary density in skeletal muscle that is more pronounced in the pregnant state, which may contribute to the vascular pathophysiology observed in preeclampsia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".