Low Serum-Derived Syncytin-2 Levels in Exosome at Early Pregnancy is a Predictor of Preeclampsia: A Prospective Pilot Study in Benin, West Africa
Bibliographic record
Abstract
Preeclampsia (PE) affects 2 to 8% of pregnant women and represents one of the major causes of maternal and perinatal morbidity and mortality, particularly in sub-Saharan Africa. A limited number of biomarkers have been proposed for the identification of pregnant women predisposed to preeclampsia. Syncytin-2 is an endogenous retrovirus envelope protein playing a key role in placental formation through the fusion of villous cytotrophoblasts, resulting in syncytiotrophoblast formation. The reduction of Syncytin-2 levels detected in placental tissue and on the surface of exosomes has been shown to strongly correlate with the severity of symptoms in preeclamptic patients. We were thus interested in conducting an analysis of a Benin cohort of pregnant women over the predictive value of this marker. From July 2015 to January 2017, 260 pregnant women were recruited in two health facilities. Blood samples were monthly collected from the beginning of pregnancy up to 20 weeks of gestation and exosomes were then isolated. We then compared Syncytin-2 levels in exosome preparations from women who presented PE to those with normal pregnancy. Our results showed that Syncytin-2 significantly decreased between 7 to 10 weeks of gestation in pregnant women with PE compared to normal pregnant women (p=0.02). Our study thereby suggests that Syncytin-2 could be a promising biomarker for early diagnosis of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".