Role of EG‐VEGF in human placentation: Physiological and pathological implications
Bibliographic record
Abstract
Pre-eclampsia (PE), the major cause of maternal morbidity and mortality, is thought to be caused by shallow invasion of the maternal decidua by extravillous trophoblasts (EVT). Data suggest that a fine balance between the expressions of pro- and anti-invasive factors might regulate EVT invasiveness. Recently, we showed that the expression of the new growth factor endocrine gland-derived vascular endothelial growth factor (EG-VEGF) is high in early pregnancy but falls after 11 weeks, suggesting an essential role for this factor in early pregnancy. Using human villous explants and HTR-8/SVneo, a first trimester extravillous trophoblast cell line, we showed differential expression of EG-VEGF receptors, PKR1 and PKR2, in the placenta and demonstrated that EG-VEGF inhibits EVT migration, invasion and tube-like organisation. EG-VEGF inhibitory effect on invasion was supported by a decrease in matrix metalloproteinase (MMP)-2 and MMP-9 production. Interference with PKR2 expression, using specific siRNAs, reversed the EG-VEGF-induced inhibitory effects. Furthermore, we determined EG-VEGF circulating levels in normal and PE patients. Our results showed that EG-VEGF levels were highest during the first trimester of pregnancy and decreased thereafter to non-pregnant levels. More important, EG-VEGF levels were significantly elevated in PE patients compared with age-matched controls. These findings identify EG-VEGF as a novel paracrine regulator of trophoblast invasion. We speculate that a failure to correctly down-regulate placental expression of EG-VEGF at the end of the first trimester of pregnancy might lead to PE.
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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) | 0.000 | 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".