Bibliographic record
Abstract
Vascular endothelial growth factors (VEGFs) regulate blood and lymphatic vessel development and homeostasis. VEGFs are predominantly produced by endothelial, hematopoietic, and stromal cells in response to hypoxia and upon stimulation by growth factors such as transforming growth factor beta (TGFbeta), interleukins, or platelet-derived growth factors (PDGFs). VEGFs specifically interact with one or several receptor tyrosine kinases (RTKs), VEGF receptor-1, -2, and -3 (VEGFR-1, -2, -3), and with distinct coreceptors such as neuropilins or heparan sulfate glycosaminoglycans. VEGF receptors are classified as type V RTKs whose extracellular domains consists of seven immunoglobulin-like (Ig-like) domains. VEGF receptors are activated upon ligand-mediated dimerization. However, little was known about the mechanism of receptor activation at the structural level until recently. New data published by several labs for VEGF and the related type III RTKs now suggest that both ligand-receptor as well as homotypic receptor-receptor interactions stabilize ligand-induced receptor dimers. These data support the idea that structural changes induced in the extracellular domain upon ligand binding instigate transmembrane signaling by properly positioning the intracellular kinase domains in active receptor dimers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".