Uniaxial Hydroxyapatite Growth on a Self-Assembled Protein Scaffold
Bibliographic record
Abstract
Biomineralization is a crucial process whereby organisms produce mineralized tissues such as teeth for mastication, bones for support, and shells for protection. Mineralized tissues are composed of hierarchically organized hydroxyapatite crystals, with a limited capacity to regenerate when demineralized or damaged past a critical size. Thus, the development of protein-based materials that act as artificial scaffolds to guide hydroxyapatite growth is an attractive goal both for the design of ordered nanomaterials and for tissue regeneration. In particular, amelogenin, which is the main protein that scaffolds the hierarchical organization of hydroxyapatite crystals in enamel, amelogenin recombinamers, and amelogenin-derived peptide scaffolds have all been investigated for in vitro mineral growth. Here, we describe uniaxial hydroxyapatite growth on a nanoengineered amelogenin scaffold in combination with amelotin, a mineral promoting protein present during enamel formation. This bio-inspired approach for hydroxyapatite growth may inform the molecular mechanism of hydroxyapatite formation in vitro as well as possible mechanisms at play during mineralized tissue formation.
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) | 0.000 | 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 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".