Angiogenesis in the regenerating tail of the leopard gecko, <i>Eublepharis macularius</i>
Bibliographic record
Abstract
Angiogenesis has well documented roles in embryonic development and tumourigenesis, and is also involved in the phenomenon of tissue repair and regeneration. We investigated angiogenesis during complex multi-tissue regeneration using the leopard gecko tail as a novel model. Similar to many lizards, leopard geckos are able to naturally regenerate their tails following predation. Using various mammalian angiogenic markers (alpha-smooth muscle actin (α-SMA), vascular endothelial growth factor (VEGF), thrombospondin-1 (TSP-1), and cluster of differentiation 36 (CD36)) we characterized angiogenesis during reparative regeneration. After an initial wound healing phase, tail regeneration begins with the formation of an aggregation of proliferating cells (blastema). As the blastema increases in size new capillaries develop, lined by VEGF positive endothelial cells (ECs). Continued maturation of vessels corresponds with the recruitment of α-SMA positive pericytes, as well as ongoing VEGF expression in ECs. While TSP-1 is not expressed in ECs, it is seen transiently in cells of the blastema immediately following wound healing. CD36 is expressed in ECs of the late stage regenerate tail, presumably involved in TSP-1 mediated vessel remodelling. We demonstrate that regenerative angiogenesis forms an organized vascular network through a highly regulated process comparable to embryonic embryogenesis. Grant Funding Source: NSERC, CIHR
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) | 0.000 | 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".