Fibronectin and JMJD6 Signature in Circulating Placental Extracellular Vesicles for the Detection of Preeclampsia
Bibliographic record
Abstract
Preeclampsia (PE) is a major obstetric complication that is challenging to predict. Currently, there are limited tools to assess placental health/function in crucial gestational periods for diagnosis and early prediction. The glycoprotein fibronectin (FN) is augmented in PE placentae, and associated with reduced activity of JMJD6, an oxygen sensor that regulates placental FN processing. Evidence implicates placenta-derived small extracellular vesicles (sEVs) in the pathogenesis of pregnancy-associated disorders. Here, we examined the utility of FN and JMJD6 in placental sEVs as putative markers for early- and late-onset PE (E-PE and L-PE). Maternal plasma was obtained from venous blood collected longitudinally during pregnancy (10-14, 16-22, and 26-32 weeks of gestation and at delivery) in normotensive term control, preterm control, L-PE, E-PE, and gestational hypertensive individuals. Placenta-derived sEVs were isolated and their FN and JMJD6 content and JMJD6 activity were measured. In women that went on to develop preeclampsia, FN content of circulating placental sEVs was significantly elevated as early as 10 to 14 weeks of gestation and remained augmented until the time of delivery. This was accompanied by a depletion in JMJD6 content. Multivariate receiver operating characteristic analysis revealed high predictive power for FN and JMJD6 as early markers of E-PE and L-PE. In vitro, hypoxia or JMJD6 loss promoted FN accumulation in sEVs that was reverted on restoring cellular iron balance with the natural compound, Hinokitiol. Elevated FN, along with diminished JMJD6 in circulating placental sEVs, serves as an early molecular signature for the detection of different hypertensive disorders of pregnancy and their severity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".