Loss of Vascular Endothelial Growth Factor A Activity in Murine Epidermal Keratinocytes Delays Wound Healing and Inhibits Tumor Formation
Bibliographic record
Abstract
The angiogenic cytokine vascular endothelial growth factor (VEGF)-A plays a central role in both wound healing and tumor growth. In the skin, epidermal keratinocytes are a major source of this growth factor. To study the contribution of keratinocyte-derived VEGF-A to these angiogenesis-dependent processes, we generated mice in which this cytokine was inactivated specifically in keratin 5-expressing tissues. The mutant mice were macroscopically normal, and the skin capillary system was well established, demonstrating that keratinocyte-derived VEGF-A is not essential for angiogenesis in the skin during embryonic development. However, healing of full-thickness wounds in adult animals was appreciably delayed compared with controls, with retarded crust shedding and the appearance of a blood vessel-free zone underneath the newly formed epidermis. When 9,12-dimethyl 1,2-benzanthracene was applied as both tumor initiator and promoter, a total of 143 papillomas developed in 20 of 23 (87%) of control mice. In contrast, only three papillomas arose in 2 of 17 (12%) of the mutant mice, whereas the rest merely displayed epidermal thickening and parakeratosis. Mutant mice also developed only 2 squamous cell carcinomas, whereas 11 carcinomas were found in seven of the control animals. These data demonstrate that whereas keratinocyte-derived VEGF-A is dispensable for skin vascularization under physiological conditions, it plays an important albeit nonessential role during epidermal wound healing and is crucial for the development of 9,12-dimethyl 1,2-benzanthracene-induced epithelial skin tumors.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".