Ameloblastin is a cell adhesion molecule required for maintaining the differentiation state of ameloblasts
Bibliographic record
Abstract
Tooth morphogenesis results from reciprocal interactions between oral epithelium and ectomesenchyme culminating in the formation of mineralized tissues, enamel, and dentin. During this process, epithelial cells differentiate into enamel-secreting ameloblasts. Ameloblastin, an enamel matrix protein, is expressed by differentiating ameloblasts. Here, we report the creation of ameloblastin-null mice, which developed severe enamel hypoplasia. In mutant tooth, the dental epithelium differentiated into enamel-secreting ameloblasts, but the cells were detached from the matrix and subsequently lost cell polarity, resumed proliferation, and formed multicell layers. Expression of Msx2, p27, and p75 were deregulated in mutant ameloblasts, the phenotypes of which were reversed to undifferentiated epithelium. We found that recombinant ameloblastin adhered specifically to ameloblasts and inhibited cell proliferation. The mutant mice developed an odontogenic tumor of dental epithelium origin. Thus, ameloblastin is a cell adhesion molecule essential for amelogenesis, and it plays a role in maintaining the differentiation state of secretory stage ameloblasts by binding to ameloblasts and inhibiting proliferation.
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 | 0.000 | 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) | 0.001 | 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".