Pericytes Contribute to Dysfunction in a Human 3D Model of Placental Microvasculature through VEGF‐Ang‐Tie2 Signaling
Bibliographic record
Abstract
Placental vasculopathies are associated with a number of pregnancy-related diseases, including pre-eclampsia (PE)-a leading cause of maternal-fetal morbidity and mortality worldwide. Placental presentations of PE are associated with endothelial dysfunction, reduced vessel perfusion, white blood cell infiltration, and altered production of angiogenic factors within the placenta (a candidate mechanism). Despite maintaining vascular quiescence in other tissues, how pericytes contribute to vascular growth and signaling in the placenta remains unknown. Here, pericytes are hypothesized to play a detrimental role in the pathogenesis of placental vascular growth. A perfusable triculture model is developed, consisting of human endothelial cells, fibroblasts, and pericytes, capable of recapitulating growth and remodeling in a system that mimics inflamed placental microvessels. Placental pericytes are shown to contribute to growth restriction of microvessels over time, an effect that is strongly regulated by vascular endothelial growth factor and Angiopoietin/Tie2 signaling. Furthermore, this model is capable of recapitulating essential processes including tumor necrosis factor alpha (TNFα)-mediated vascular leakage and leukocyte infiltration, both important aspects associated with placental PE. This placental vascular model highlights that an imbalance in endothelial-pericyte crosstalk can play a critical role in the development of vascular pathology and associated diseases.
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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 | 0.000 | 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 | 0.001 | 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".