Increased expression of sFlt-1 in in vivo and in vitro models of human placental hypoxia is mediated by HIF-1
Bibliographic record
Abstract
Elevated expression of soluble vascular endothelial growth factor receptor-1 (sFlt-1) in preeclampsia plays a major role in the pathogenesis of this serious disorder of human pregnancy. Although reduced placental oxygenation is thought to be involved in the pathogenesis of preeclampsia, it is unclear how oxygen regulates placental sFlt-1 expression. The aims herein were to investigate sFlt-1 expression in in vivo and in vitro physiological and pathological models of human placental hypoxia and to understand the role of hypoxia inducible factor-1 (HIF-1) in regulating the expression of this molecule. sFlt-1 expression in placental villi was significantly increased under physiological low oxygen conditions in early first-trimester and in high-altitude placentae, as well as in pathological low oxygen conditions, such as preeclampsia. In high-altitude and in preeclamptic tissue, sFlt-1 localized within villi to perivascular regions, the syncytiotrophoblast layer, and syncytial knots. In first-trimester villous explants, low oxygen, but not hypoxia-reoxygenation (HR), increased sFlt-1 expression. Moreover, exposure of villous explants to dimethyloxalyl-glycin, a pharmacological inhibitor of prolyl-hydroxylases, which mimics hypoxia by increasing HIF-1alpha stability, increased sFlt-1 expression. Conversely, HIF-1alpha knockdown using antisense oligonucleotides, decreased sFlt-1 expression. In conclusion, placental sFlt-1 expression is increased by both physiologically and pathologically low levels of oxygen. This oxygen-induced effect is mediated via the transcription factor HIF-1. Low oxygen levels, as opposed to intermittent oxygen tension (HR) changes, play an important role in regulating sFlt-1 expression in the developing human placenta and hence may contribute to the development of preeclampsia.
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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 | 0.001 |
| 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".