Axolotl as a Model to Study Scarless Wound Healing in Vertebrates: Role of the Transforming Growth Factor Beta Signaling Pathway
Bibliographic record
Abstract
SIGNIFICANCE: The skin is our largest organ, with the primary role of protection against assaults from the outside world. It also suffers frequent damage, from minor scrapes to, more rarely, complete destruction such as in third-degree burns. It is therefore, by its nature, an organ that would benefit tremendously from being able to regenerate itself. RECENT ADVANCES: This review highlights the axolotl, a less well-known model organism capable of scarless wound healing and regeneration. Axolotls are salamanders with unsurpassed healing and regenerative capacities. Understanding how these animals can regenerate their tissues could help identify the pathways that need to be activated or inhibited in humans to improve wound healing. CRITICAL ISSUES: Presently, there are no therapies leading to skin regeneration or scarless wound healing. Various animal models have thus been developed for use in research, such as mice and pigs, to help us understand how wound healing could be improved or stimulated. However, these more common models cannot regenerate and, consequently, cannot direct us toward a solution to regenerate damaged tissues. Axolotls, on the other hand, can regenerate perfectly and therefore may offer avenues to identify molecular targets for therapeutic intervention. FUTURE DIRECTIONS: Identifying signaling pathways regulating tissue regeneration in vertebrate models is important. The use of animals such as axolotls, which hold the secret of full regeneration, will likely play a significant role in helping us achieve scarless wound healing for humans.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".