Ferroptosis as a mechanism of placenta dysfunction in inflammation-driven preeclampsia
Bibliographic record
Abstract
BACKGROUND: Preeclampsia (PE) is a hypertensive pregnancy syndrome with significant clinical and pathological diversity, linked to distinct etiological subclasses. One etiological subclass of PE, characterized by increased inflammation at the maternal-fetal interface (I-PE), is strongly associated with preterm birth and fetal growth restriction, though its specific pathophysiology remains poorly understood. Inflammatory signals can induce iron overload, leading to ferroptosis-a programmed cell death process. Dysregulation of systemic and placental iron metabolism has been described in PE when considered as a single clinical entity, but previous studies have not accounted for distinct underlying etiologies. This study investigates the role of ferroptosis signaling in placental dysfunction across different PE subclasses. METHODS: Histological analysis assessed placental iron accumulation and ferritin protein expression. Placental gene expression was evaluated for ferroptosis-related genes (FRGs) using gene set enrichment analysis (GSEA) on placenta samples from healthy controls and three previously described PE subclasses. Digital cytometry estimated cell type-specific expression of FRGs across these subclasses. RESULTS: Significant placenta iron accumulation and reduced ferritin expression were found exclusively in I-PE subclass. GSEA showed enrichment of FRGs across various functional categories, including regulators, markers, suppressors, and unclassified FRGs in the placentas from I-PE. Digital cytometry indicated disrupted FRG expression in trophoblasts and mesodermal stromal cells in these placentas, consistent with histologically observed iron accumulation. CONCLUSION: Placental iron accumulation and disrupted ferroptosis signaling in I-PE subclass suggests a novel mechanism of placental dysfunction unique to this subclass. Further research is needed to explore how regulating ferroptosis could aid in managing I-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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".