<scp>VEGF</scp>,<scp>FGF</scp>‐2 and<scp>TGF</scp>β expression in the normal and regenerating epidermis of geckos: implications for epidermal homeostasis and wound healing in reptiles
Bibliographic record
Abstract
The skin is a bilayered organ that serves as a key barrier between an organism and its environment. In addition to protecting against microbial invasion, physical trauma and environmental damage, skin participates in maintaining homeostasis. Skin is also capable of spontaneous self-repair following injury. These functions are mediated by numerous pleiotrophic growth factors, including members of the vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF), and transforming growth factor β (TGFβ) families. Although growth factor expression has been well documented in mammals, particularly during wound healing, for groups such as reptiles less is known. Here, we investigate the spatio-temporal pattern of expression of multiple growth factors in normal skin and following a full-thickness cutaneous injury in the representative lizard Eublepharis macularius, the leopard gecko. Unlike mammals, leopard geckos can heal cutaneous wounds without scarring. We demonstrate that before, during and after injury, keratinocytes of the epidermis express a diverse panel of growth factor ligands and receptors, including: VEGF, VEGFR1, VEGFR2, and phosphorylated VEGFR2; FGF-2 and FGFR1; and phosphorylated SMAD2, TGFβ1, and activin βA. Unexpectedly, only the tyrosine kinase receptors VEGFR1 and FGFR1 were dynamically expressed, and only during the earliest phases of re-epithelization; otherwise all the proteins of interest were constitutively present. We propose that the ubiquitous pattern of growth factor expression by keratinocytes is associated with various roles during tissue homeostasis, including protection against ultraviolet photodamage and coordinated body-wide skin shedding.
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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".