Characterizing placental pericytes: Hypoxia and proangiogenic signalling
Bibliographic record
Abstract
INTRODUCTION: Pericytes wrap microvessels and interact with endothelial cells to regulate vascular growth. Though pericyte dropout has been reported in pathological human placentae and mouse models of placental pathology, there has been limited investigation of the role and function of placental pericytes in vascular health and pathology. This study aimed to investigate the angiogenic potential of human placental pericytes relative to other villous cell populations. METHODS: or ambient air, followed by analysis of secreted angiogenic factors (ELISA). Additionally, the placental pericytes and HUVECs were co-cultured in a 3D sprouting assay to assess the capacity of pericytes to contribute to vascular sprouts. RESULTS: affected secretion of angiogenic factors in placental pericytes, HUVECs, and syncytialized BeWo cells. Specifically, in placental pericytes, angiopoietin-1 (ANG1) and soluble fms-like tyrosine kinase-1 (sFLT1) were decreased, while vascular endothelial growth factor (VEGF) was increased. In HUVECS, matrix metalloproteinase-2 (MMP2), VEGF, angiopoietin-2 (ANG2), platelet-derived growth factor beta (PDGFB), placental growth factor (PlGF), and sFLT1 were increased. In syncytialized BeWo cells, VEGF, MMP2, PDGFB, PlGF, and sFLT1 secretion were increased. Placental pericytes and HUVECS colocalized to vessel sprouts in the 3-D sprouting assay. DISCUSSION: Hypoxic conditions altered placental pericyte, endothelial, and syncytialized BeWo secretion of angiogenic factors. We speculate that pericyte dropout and, by extension, the loss of pericyte-derived angiogenic factors in hypoxic conditions may contribute to compromised fetal vascular development observed in placental pathologies.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".