Axolotls’ and Mices’ Oral-Maxillofacial Trephining Wounds Heal Differently
Bibliographic record
Abstract
The Ambystoma maxicanum (axolotl) regenerates strikingly from wounds and amputations. Comparing its healing ability to non-regenerative species such as the mouse should help narrow in on mechanisms to improve human wound healing. Here, the tongue and intermandibular soft tissues of both mice (C57BL/6NCrl) and axolotls were wounded with a 2-2.5 mm punch biopsy. The study aimed to compare the differences between these 2 species following surgical resection with regard to the macroscopic and histological characteristics. These include wound closure times, epithelial wound sealing and thickness as well as acute immune marker myeloperoxidase (MPO) response over 30 days. Post surgery, mice visually showed greater haemorrhage; their wounds immediately collapsed while it took 14 days for the axolotls mandibular void to close. The epithelium sealed the axolotls' wound margins within 24 h with a maximal mean thickness of 0.42 ± 0.13-fold normalized to unwounded skin. In mice, the epithelium separately sealed the ventral and dorsal sides, respectively at 7 and 7-30 days with mean maximal epithelial thicknesses reaching 13 ± 5.6 and 3.0 ± 0.63-fold. Mean MPO-positive cell values peaked in axolotls at 14 ± 1.5-fold between hours 6-12; while in mice, it peaked at 8.7 ± 0.9-fold between hours 24-96. We conclude that axolotls form smaller blood clots, have a faster and thinner epithelial cell migrating front, and a shorter MPO-positive cell response in comparison to mice. These observations may help refine future oral and facial wound-healing research and treatment.
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 | 0.001 | 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) | 0.003 | 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".