Friends Turned Foes: Angiogenic Growth Factors beyond Angiogenesis
Bibliographic record
Abstract
Angiogenesis, the formation of new blood vessels from pre-existing ones is a biological process that ensures an adequate blood flow is maintained to provide the cells with a sufficient supply of nutrients and oxygen within the body. Numerous soluble growth factors and inhibitors, cytokines, proteases as well as extracellular matrix proteins and adhesion molecules stringently regulate the multi-factorial process of angiogenesis. The properties and interactions of key angiogenic molecules such as vascular endothelial growth factors (VEGFs), fibroblast growth factors (FGFs) and angiopoietins have been investigated in great detail with respect to their molecular impact on angiogenesis. Since the discovery of angiogenic growth factors, much research has been focused on their biological actions and their potential use as therapeutic targets for angiogenic or anti-angiogenic strategies in a context-dependent manner depending on the pathologies. It is generally accepted that these factors play an indispensable role in angiogenesis. However, it is becoming increasingly evident that this is not their only role and it is likely that the angiogenic factors have important functions in a wider range of biological and pathological processes. The additional roles played by these molecules in numerous pathologies and biological processes beyond angiogenesis are discussed in this review.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".