Impact of acute moderate-intensity aerobic exercise on circulating extracellular vesicles in pregnant and non-pregnant women
Bibliographic record
Abstract
Exercise improves cardiovascular and metabolic health in pregnancy and may represent a non-pharmacological approach to improving pregnancy outcomes. Extracellular vesicles (EVs) are emerging biomarkers of endothelial dysfunction and offer the potential for evaluating vascular health non-invasively during pregnancy. The purpose of this study was to investigate changes in circulating EV levels after an acute bout of moderate-intensity aerobic exercise in healthy pregnant and non-pregnant women. We studied plasma samples from pregnant ( N = 13, 13–28 weeks) and non-pregnant ( N = 17) women. A pre-exercise blood sample was obtained followed by a 30 min bout of moderate-intensity treadmill-based exercise. Immediately following the exercise, a post-exercise blood draw was collected. Large EVs were isolated from plasma by differential centrifugation and characterized by Western blot and electron microscopy. We quantified circulating EVs by nanoscale flow cytometry. Endothelial EVs were identified as VE-Cadherin+, platelet EVs as CD41+, and leukocyte EVs as CD45+ events. Acute exercise was associated with a significant reduction in levels of circulating endothelial EVs in the non-pregnant group ( p = 0.0232) but not in the pregnant group ( p = 0.2734). A greater proportion of non-pregnant women (13/17, 76.47%) exhibited a reduction in endothelial EVs compared with their pregnant counterparts (4/13, 30.76%, p < 0.05). We also observed a positive association between measures of fitness (average speed) and baseline levels of platelet ( r = 0.5816, p = 0.0159) and total EVs ( r = 0.5325, p = 0.0296) in the non-pregnant group but not in pregnant individuals. Collectively, our study highlights that after a matched acute exercise, changes to circulating EV levels differ depending on pregnancy status.
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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 | 0.000 |
| 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".