Modification of angiogenic factors by regular and acute exercise during pregnancy
Bibliographic record
Abstract
This cross-sectional study examined mechanisms through which exercise might alter preeclampsia risk by estimating the effects of acute and chronic exercise on angiogenic markers in healthy pregnant women with different amounts of regular exercise participation. Serum-soluble fms-like tyrosine kinase-1 (sFlt-1), placental growth factor (PlGF), and soluble endoglin (sEng) were measured before and after 20 min of moderate-intensity cycle ergometry in normotensive, nonsmoking pregnant (16 active, 9 inactive, 34.1+/-1.6 wk gestation) and nonpregnant (15 active, 12 inactive, midlate luteal phase) women. Inactive women did not regularly exercise at an intensity that was sufficient to cause sweating. Active women exercised for at least 3 h/wk. Inactive pregnant women had significantly lower PlGF concentrations [median (interquartile range): 268 (159, 290) vs. 278 (221, 647) pg/ml, P=0.014] and higher sFlt-1 [5,180 (4,540, 5,834) vs. 4,217 (2,014, 5,481) pg/ml, P=0.005] and sEng concentrations [9.1 (7.7, 16.7) vs. 7.8 (6.5, 10.1) ng/ml, P=0.025] than active pregnant women. This effect of regular exercise participation was not observed in nonpregnant women. Acute exercise in pregnancy was not associated with antiangiogenic changes that might contribute to preeclampsia; rather, there was a small, but statistically significant, increase in PlGF following acute exercise in active pregnant women [278 (221, 647) vs. 335 (245, 628) pg/ml, P=0.014]. sFlt-1 increased significantly following acute exercise in inactive nonpregnant women [90 (86, 100) vs. 106 (101, 116) pg/ml, P=0.012], but not in active nonpregnant women. Regular exercise during pregnancy is associated with higher serum PlGF and lower sFlt-1 and sEng concentrations in late gestation, a difference that is unlikely to have predated the pregnancy.
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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) | 0.000 | 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".