A potential role for autocrine VEGF signaling in endothelial cell function
Bibliographic record
Abstract
Paracrine‐derived vascular endothelial growth factor (VEGF) is established as an indispensable contributor to the angiogenic cascade. Autocrine VEGF, in contrast, is solely attributed to endothelial cell survival signaling, and its potential regulation of other endothelial cell functions has not been studied. We hypothesized that the deletion of VEGF would disrupt endothelial cell migration and shear stress adaptation. Endothelial cells were isolated from transgenic mice having VEGF exon 3 flanked by loxP sites (VEGF L/L ). Cells were transduced with adeno‐Cre recombinase to induce VEGF deletion, or adeno‐β‐galactosidase as a control. When subjected to a scrape migration assay, cells with Cre induced VEGF deletion (VEGFΔ) exhibited impaired cell motility compared VEGF L/L cells (12% compared to 64% wound closure after 48 hours; p<0.05). Shear stress stimulation (15 dynes; 2hr) caused significant increases in phospho‐Akt in VEGF L/L cells, which was repressed in VEGFΔ cells (2.4 vs. 1.5 fold above static, respectively). Shear‐induced increases in phospho‐p38MAPK were unaltered in VEGFΔ cells (3.9 vs 3.3 fold above static, respectively). Shear‐induced endothelial cell alignment (15 dynes; 24hr) also was repressed in VEGFΔ cells. Taken together these results provide novel evidence that autocrine VEGF contributes to multiple aspects of endothelial cell function.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".