Evidence Implicating Peroxisome Proliferator-Activated Receptor-γ in the Pathogenesis of Preeclampsia
Bibliographic record
Abstract
Preeclampsia, a major cause of maternal and perinatal mortality and morbidity, is thought to be attributed, in part, to impaired trophoblast invasion. Peroxisome proliferator-activated receptors are ligand-activated transcription factors expressed in trophoblasts, which regulate the expression of a number of genes involved in cell differentiation and proliferation. We investigated the effect of the administration of a peroxisome proliferator-activated receptor-γ antagonist during uncomplicated pregnancy in rats. Using an intraperitoneal miniosmotic pump, healthy pregnant rats were administered either vehicle or the peroxisome proliferator-activated receptor-γ-specific antagonist, T0070907 (1 mg/kg per day from gestational days 11-15). Rats treated with T0070907 developed key features of preeclampsia, including elevated mean arterial blood pressure, proteinuria, endothelial dysfunction, reduced pup weight, and increased platelet aggregation. T0070907-treated rats had reduced plasma vascular endothelial growth factor and increased plasma soluble fms-like tyrosine kinase 1. Furthermore, increases in total placental soluble fms-like tyrosine kinase 1 mRNA and fms-like tyrosine kinase 1 protein were also demonstrated, suggesting the placenta as the main contributor to the increased circulating levels of soluble fms-like tyrosine kinase 1. The labyrinthine trophoblast in the placentas of T0070907-treated rats were less differentiated, had increased cellular proliferation, and were strongly immunopositive for CD-31 staining, indicating adaptive angiogenesis. The present study suggests that peroxisome proliferator-activated receptor-γ may play a pivotal role in the progression of a healthy pregnancy and may critically regulate the risk of preeclampsia. These findings have important implications regarding the underlying etiology of preeclampsia and potential therapeutic targets.
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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 | 0.001 | 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".