Effects of glycol-split low molecular weight heparin on placental, endothelial, and anti-inflammatory pathways relevant to preeclampsia†
Bibliographic record
Abstract
Low molecular weight heparin (LMWH) is being investigated as a potential preventative therapy against preeclampsia. There is evidence suggesting that LMWH may prevent preeclampsia through anticoagulation-independent mechanisms. In this study, we compared the in vitro placental, endothelial, and anti-inflammatory effects of an LMWH (dalteparin) with a nonanticoagulant, glycol-split heparin derivative (gsHep). In contrast with dalteparin, gsHep did not interact with antithrombin III, possess significant anti-Factor Xa activity, or significantly prolong in vitro plasma clotting time. However, dalteparin and gsHep were otherwise mechanistically similar, both interacting with soluble fms-like tyrosine kinase-1 (sFlt1) and promoting release of the pro-angiogenic protein placental growth factor, but not the antiangiogenic sFlt1, from healthy placental villous explants. Placental explant media pretreated with dalteparin or gsHep significantly stimulated endothelial cell tube formation compared to untreated explants. Lastly, dalteparin and gsHep both significantly suppressed inflammation by inhibiting complement activation and leukocyte adhesion to endothelial cells that were activated using serum from preeclamptic women. Our data suggest that nonanticoagulant heparin derivatives may be utilized as a tool to distinguish the anticoagulation-independent mechanisms of LMWH, and provide insight into the role of anticoagulation in the prevention of 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".