MicroRNA Control of Vascular Endothelial Growth Factor Signaling Output During Vascular Development
Bibliographic record
Abstract
The regulated response of endothelial cells to signals in their environment is not only critical for the de novo formation of primordial vascular networks during early development (ie, vasculogenesis), but is also required for the subsequent growth and remodeling of new blood vessels from preexisting ones (ie, angiogenesis). Vascular endothelial growth factors (Vegfs) and their endothelial cell-specific receptors play a crucial role in nearly all aspects of blood vessel growth. How the outputs from these pathways affect and coordinate endothelial behavior is an area of intense research. Recently, numerous studies have highlighted roles for microRNAs in modulating Vegf signaling output in several different contexts. In this review, we will provide an overview of how small RNAs regulate multiple aspects of the Vegf signaling pathway. In particular, we highlight areas where identification of microRNAs and their targets has provided new insight into the role of downstream effectors in modulating Vegf output during development. As Vegf plays a broad role in multiple aspects of endothelial biology and has become a target for therapeutic manipulation of pathological blood vessel growth, microRNAs that affect Vegf signaling output will undoubtedly be major targets of clinical value.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".