Functional Differences Between Placental Micro- and Macrovascular Endothelial Colony-Forming Cells
Bibliographic record
Abstract
Alterations in the development of the placental vasculature can lead to pregnancy complications, such as preeclampsia. Currently, the cause of preeclampsia is unknown, and there are no specific prevention or treatment strategies. Further insight into the placental vasculature may aid in identifying causal factors. Endothelial colony-forming cells (ECFCs) are a subset of endothelial progenitor cells capable of self-renewal and de novo vessel formation in vitro. We hypothesized that ECFCs exist in the micro- and macrovasculature of the normal, term human placenta. Human placentas were collected from term pregnancies delivered by cesarean section (n = 16). Placental micro- and macrovasculature was collected from the maternal and fetal side of the placenta, respectively, and ECFCs were isolated and characterized. ECFCs were CD31(+), CD105(+), CD144(+), CD146(+), CD14(-), and CD45(-), took up 1,1'-dioctadecyl-3,3,3',3'-tetramethyl-indocarbocyanine perchlorate-labeled acetylated low-density lipoprotein, and bound Ulex europaeus agglutinin 1. In vitro, macrovascular ECFCs had a greater potential to generate high-proliferative colonies and formed more complex capillary-like networks on Matrigel compared with microvascular ECFCs. In contrast, in vivo assessment demonstrated that microvascular ECFCs had a greater potential to form vessels. Macrovascular ECFCs were of fetal origin, whereas microvascular ECFCs were of maternal origin. ECFCs exist in the micro- and macrovasculature of the normal, term human placenta. Although macrovascular ECFCs demonstrated greater vessel and colony-forming potency in vitro, this did not translate in vivo, where microvascular ECFCs exhibited a greater vessel-forming ability. These important findings contribute to the current understanding of normal placental vascular development and may aid in identifying factors involved in preeclampsia and other pregnancy complications.
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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 | 0.000 | 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".