The antithrombin binding regions of heparin mediate fetal growth and reduced placental damage in the RUPP model of preeclampsia†
Bibliographic record
Abstract
Preeclampsia is a serious hypertensive disorder of pregnancy, which is only cured with delivery of the placenta, thereby commonly necessitating preterm birth of the fetus. Low-molecular-weight heparin (LMWH) has demonstrated potential to reduce the incidence of preeclampsia in high-risk pregnant women, although the underlying mechanism by which LMWH protects against preeclampsia is unknown. Given the complex structure and biologic actions of heparin, we tested the hypothesis that heparin can mediate preeclampsia prevention via nonanticoagulant pathways. We compared the effects of a nonanticoagulant, glycol-split LMWH (gsHep)-rendered nonanticoagulant through disruption of the antithrombin binding regions-with the LMWH dalteparin in the rat reduced uterine perfusion pressure (RUPP) surgical model of preeclampsia. Although RUPP animals exhibit significantly elevated blood pressure and reduced plasma levels of placental growth factor (PGF) compared to sham, neither dalteparin nor gsHep treatment significantly impacted these parameters. However, the observed positive correlation between PGF levels and number of viable fetuses in RUPP-induced animals suggests that reduced PGF levels were predominately due to placental loss. Daily subcutaneous injections of low-dose dalteparin but not gsHep significantly restored fetal growth that was impaired by RUPP surgery. Placentas from RUPP animals exhibited an abnormal labyrinth structure, characterized by expanded sinusoidal blood spaces, relative to sham-operated animals. Morphometric analysis demonstrated that dalteparin but not gsHep treatment normalized development of the labyrinth in RUPP-exposed conceptuses. These data suggest that the antithrombin-binding regions of LMWH are required to confer its protective effects on fetal growth and placental development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".