A Brief History of the Discovery of Amelogenin Nanoribbons In Vitro and In Vivo
Bibliographic record
Abstract
Without evidence for an organic framework, biological and biochemical processes observed during amelogenesis provided limited information on how extracellular matrix proteins control the development of the complex fibrous architecture of human enamel. Over a decade ago, amelogenin nanoribbons were first observed from recombinant proteins during in vitro mineralization experiments in our laboratory. In enamel from mice lacking the enzyme kallikrein 4 (KLK4), we later uncovered ribbon-like protein structures that matched the morphology, width, and thickness of the nanoribbons assembled by recombinant proteins. Interestingly, similar structures had already been described since the 1960s, when enamel sections from various mammals were demineralized and stained for transmission electron microscopy analysis. However, at that time, researchers were not aware of the ability of amelogenin to form nanoribbons and instead associated the filamentous nanostructures with possible imprints of mineral ribbons in the gel-like matrix of developing enamel. Further evidence for the significance of amelogenin nanoribbons for enamel development was stipulated when recent mineralization experiments succeeded in templating and orienting the growth of apatite ribbons along the protein nanoribbon framework. This article provides a brief historical review of the discovery of amelogenin nanoribbons in our laboratory in the context of reports by others on similar structures in the developing enamel matrix.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".