Hydroxyapatite Growth on Amelogenin‐Amelotin Recombinamers
Bibliographic record
Abstract
Abstract Strategies to improve hydroxyapatite mineralization for enamel repair are essential for tissue regeneration. Amelogenin and amelotin (AMTN) are enamel matrix proteins playing critical roles in enamel formation. Amelogenin acts as a scaffold for hydroxyapatite, while AMTN (specifically its ‘SSEEL’ domain) is necessary for proper enamel mineralization. The functional relationship between recombinant AMTN and amelogenin, and their combined ability to guide uniaxial hydroxyapatite growth in vitro has been investigated recently. However, incorporation of the active domain of AMTN within recombinant amelogenins has not been studied yet. Here we describe the synthesis of modified amelogenin by inserting, internally and at its C‐terminus, the mineralizing AMTN‐derived motif SSEEL. C‐terminus modified amelogenin promoted hydroxyapatite formation, whereas internal incorporation of the motif initially resulted in amorphous calcium phosphate formation. Here we show that modified amelogenin with the mineralizing AMTN‐motif SSEEL can promote hydroxyapatite growth. These results give new insights for mineralized tissue regeneration using recombinamer proteins.
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".