Platelets Link Coagulation and Complement in Regulating Placental Vascular Development
Bibliographic record
Abstract
Abstract During early pregnancy, maternal blood surrounds the embryo before the placenta is fully developed, requiring tight regulation of maternal blood flow into the placental vasculature. We identify placental microthrombi (PMTs) as essential structures guiding this process. PMTs contain platelets, coagulation factors, and complement proteins, and their formation depends on maternal platelet activation by thrombin through the protease-activated receptor PAR4. Deficiency of PAR4 abolished PMTs and caused excessive bleeding at the implantation site. C3 deficiency also led to increased bleeding events, indicating that complement activation contributes to thrombosis in the placental circulation. Conversely, dysregulated complement activation in CMP-sialic acid synthase–deficient ( Cmas −/– ) mice led to widespread thrombosis and failed placental development. Strikingly, platelet activation via PAR4 was necessary to localize complement activation to trophoblast surfaces, thereby coupling coagulation and complement in PMT formation. Depletion of maternal platelets mitigated complement-driven thromboinflammation in Cmas −/– pregnancies, restoring placental growth. These findings uncover a critical cooperation between platelets, coagulation, and complement in establishing maternal blood flow to the placenta. Successful pregnancy therefore requires not only activation but also tight regulation of these systems to balance necessary PMT formation with the prevention of pathological thrombosis. Graphical abstract (Created in BioRender. Tiede, A. (2025) https://BioRender.com/l6zeezr )
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".