HIF1 inhibitor acriflavine rescues early-onset preeclampsia phenotype in mice lacking placental prolyl hydroxylase domain protein 2
Bibliographic record
Abstract
Preeclampsia is a serious pregnancy disorder that lacks effective treatments other than delivery. Improper sensing of oxygen changes during placentation by prolyl hydroxylases (PHDs), specifically PHD2, causes placental hypoxia-inducible factor-1 (HIF1) buildup and abnormal downstream signaling in early-onset preeclampsia, yet therapeutic targeting of HIF1 has never been attempted. Here we generated a conditional (placenta-specific) knockout of Phd2 in mice (Phd2-/- cKO) to reproduce HIF1 excess and to assess anti-HIF therapy. Conditional deletion of Phd2 in the junctional zone during pregnancy increased placental HIF1 content, resulting in abnormal placentation, impaired remodeling of the uterine spiral arteries, and fetal growth restriction. Pregnant dams developed new-onset hypertension at midgestation (E9.5) in addition to proteinuria and renal and cardiac pathology, hallmarks of severe preeclampsia in humans. Daily injection of acriflavine, a small molecule inhibitor of HIF1, to pregnant Phd2-/- cKO mice from E7.5 (prior to hypertension) or E10.5 (after hypertension had been established) to E14.5 corrected placental dysmorphologies and improved fetal growth. Moreover, it reduced maternal blood pressure and reverted renal and myocardial pathology. Thus, therapeutic targeting of the HIF pathway may improve placental development and function, as well as maternal and fetal health, in preeclampsia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".