Tail regeneration and other phenomena of wound healing and tissue restoration in lizards
Bibliographic record
Abstract
Wound healing is a fundamental evolutionary adaptation with two possible outcomes: scar formation or reparative regeneration. Scars participate in re-forming the barrier with the external environment and restoring homeostasis to injured tissues, but are well understood to represent dysfunctional replacements. In contrast, reparative regeneration is a tissue-specific program that near-perfectly replicates that which was lost or damaged. Although regeneration is best known from salamanders (including newts and axolotls) and zebrafish, it is unexpectedly widespread among vertebrates. For example, mice and humans can replace their digit tips, while many lizards can spontaneously regenerate almost their entire tail. Whereas the phenomenon of lizard tail regeneration has long been recognized, many details of this process remain poorly understood. All of this is beginning to change. This Review provides a comparative perspective on mechanisms of wound healing and regeneration, with a focus on lizards as an emerging model. Not only are lizards able to regrow cartilage and the spinal cord following tail loss, some species can also regenerate tissues after full-thickness skin wounds to the body, transections of the optic nerve and even lesions to parts of the brain. Current investigations are advancing our understanding of the biological requirements for successful tissue and organ repair, with obvious implications for biomedical sciences and regenerative medicine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".