Physical exercise for a healthy pregnancy: the role of placentokines and exerkines
Bibliographic record
Abstract
Complications such as diabetes and preeclampsia can occur during pregnancy. Moderate-intensity exercise can prevent such complications by releasing placentokines and exerkines, such as apelin , adiponectin , leptin, irisin , and chemerin . Exercise and apelin increase thermogenesis and glucose uptake in pregnancy by activating AMPK , PI3K, PGC-1α, AKT1, UCP3 , and sarcolipin. Exercise increases apelin levels to reduce preeclampsia symptoms by increasing eNOS , NO , placental growth factor (PlGF), and VEGF and decreasing levels of fms-like tyrosine kinase 1 (sFlt-1), soluble endoglin (sEng), and oxidative stress . A negative relationship has been reported between plasma leptin and VO 2 peak/kg and VO 2 peak in women with gestational diabetes . In active women, decreases in leptin levels reduce the risk of preeclampsia by ~ 40%. Higher adiponectin levels are associated with greater physical activity and lead to increased insulin sensitivity . Increased adiponectin levels in preeclampsia and exercise counteract inflammatory and atherogenic activities while also having vascular protective effects. Exercise increases irisin levels that correlate negatively with fasting glucose, insulin concentration, and glycosylated hemoglobin levels. Irisin augments mRNA expression levels of UCP1 and cell death-inducing DNA fragmentation factor-like effector A (cidea) to cause browning of adipose tissue , increased thermogenesis, and increased energy consumption. Irisin concentrations in mothers with preeclampsia in the third trimester negatively correlate with systolic and diastolic blood pressure . Expression levels of chemerin , IL-6, and TNF-α are increased in gestational diabetes , and the increases in chemerin in late pregnancy positively correlate with the ratio of sFlt-1 to PlGF as a marker of preeclampsia. The effects of physical exercise on placentokines and exerkines in women at various stages of pregnancy remain poorly understood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".