Tissue regeneration in dentistry: Can salamanders provide insight?
Bibliographic record
Abstract
The ability to regenerate damaged tissues would be of tremendous benefit for medicine and dentistry. Unfortunately, humans are unable to regenerate tissues such as teeth and fingers or to repair injured spinal cord. With an aging population, health problems are more prominent and dentistry is no exception as loss of bone tissue in the orofacial sphere from periodontal disease is on the rise. Humans can repair oral soft tissues exceptionally well; however, hard tissues, such as bone and teeth, are devoid of the ability to repair well or at all. Fortunately, Mother Nature has solved nearly every problem that we would like to solve for our own benefit and tissue regeneration is no exception. By studying animals that can regenerate, like Axolotls (Mexican salamander), we hope to find ways to stimulate regeneration in humans. We will discuss the role of the transforming growth factor beta cytokines as they are central to wound healing in humans and regeneration in Axolotls. We will also compare wound healing in humans (skin and oral mucosa) to Axolotl skin wound healing and limb regeneration. Finally, we will address the problem of bone regeneration and present results in salamanders which indicate that in order to regenerate bone you need to recruit non-bone cells. Fundamental research, such as the work being performed in animals that can regenerate, offers insight to help understand why some treatments are successful while others fail when it comes to specific tissues such as bones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".