Signaling pathways that regulate blood vessel morphogenesis
Bibliographic record
Abstract
We study how signals are integrated to regulate morphogenetic processes during blood vessel formation, and how cell division and morphogenesis are co‐ordinated. We utilize a stem cell model that allows for in vitro formation of blood vessels via vasculogenesis and angiogenic sprouting. We have incorporated GFP reporters into the developing vessels for dynamic imaging. Using these reagents, we have studied the role of the VEGF receptor flt‐1 (VEGFR‐1) in vessel morphogenesis using cells lacking flt‐1. Our data shows that flt‐1 normally negatively regulates endothelial cell division, but it positively regulates sprouting morphogenesis. This occurs primarily through the activity of a soluble form of the receptor (sflt‐1), as rescue with this isoform is more efficient than rescue with membrane‐localized isoforms. We also investigate the role of a Ras/Rap activator called RasGRP3. RasGRP3 is a novel endothelial DAG/phorbol ester receptor that mediates a florid dysmorphogenesis upon treatment of embryonic vessels with phorbol esters. The pathway downstream of RasGRP3 resembles a pathway that leads to vessel permeability. Thus we hypothesize that RasGRP3, which is not required for viability, is important in pathological situations to regulate permeability. RasGRP3 expression is regulated by VEGF, and we are currently investigating whether it is functionally downstream of effects of VEGF on morphogenesis and permeability.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".