Identification of putative dental epithelial stem cells in a lizard with life-long tooth replacement
Bibliographic record
Abstract
Most dentate vertebrates, including humans, replace their teeth and yet the process is poorly understood. Here, we investigate whether dental epithelial stem cells exist in a polyphyodont species, the leopard gecko (Eublepharis macularius). Since the gecko dental epithelium lacks a histologically distinct site for stem cells analogous to the mammalian hair follicle bulge, we performed a pulse-chase experiment on juvenile geckos to identify label-retaining cells (LRCs). We detected LRCs exclusively on the lingual side of the dental lamina, which exhibits low proliferation rates and is not involved in tooth morphogenesis. Lingual LRCs were organized into pockets of high density close to the successional lamina. A subset of the LRCs expresses Lgr5 and other genes that are markers of adult stem cells in mammals. Also similar to mammalian stem cells, the LRCs appear to proliferate in response to gain of function of the canonical Wnt pathway. We suggest that the LRCs in the lingual dental lamina represent a population of stem cells, the immediate descendents of which form the successional lamina and, ultimately, the replacement teeth in the gecko. Furthermore, their location on the non-tooth-forming side of the dental lamina implies that dental stem cells are sequestered from signals that might otherwise induce them to differentiate.
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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 | 0.001 | 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 | 0.000 |
| 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".