Physical activity differentially regulates VEGF, PlGF, and their receptors in the human placenta
Bibliographic record
Abstract
Physical activity (PA) has beneficial effects on the function of many organs by modulating their vascular development. Regular PA during pregnancy is associated with favorable short- and long-term outcomes for both mother and fetus. During pregnancy, appropriate vascularization of the placenta is crucial for adequate maternal-fetal nutrient and gas exchange. How PA modulates angiogenic factors, VEGF, and its receptors in the human placenta, is as of yet, unknown. We objectively measured the PA of women at 24-28 and 34-38 weeks of gestation. Participants were considered "active" if they had met or exceeded 150 min of moderate-intensity PA per week during their 2nd trimester. Term placenta tissues were collected from active (n = 23) or inactive (n = 22) women immediately after delivery. We examined the expression of the angiogenic factors VEGF, PlGF, VEGFR-1, and VEGFR-2 in the placenta. Western blot analysis showed VEGF and its receptor, VEGFR-1 was significantly (p < 0.05) higher both at the protein and mRNA levels in placenta from physically active compared to inactive women. No difference in VEGFR-2 was observed. Furthermore, immunohistochemistry showed differential staining patterns of VEGF and its receptors in placental endothelial, stromal, and trophoblast cells and in the syncytial brush border. In comparison, PlGF expression did not differ either at the protein or mRNA level in the placenta from physically active or inactive women. The expression and localization pattern of VEGF and its receptors suggest that PA during pregnancy may support a pro-angiogenic milieu to the placental vascular network.
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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) | 0.000 | 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 | 0.000 |
| 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".