Oriented and Ordered Biomimetic Remineralization of the Surface of Demineralized Dental Enamel Using HAP@ACP Nanoparticles Guided by Glycine
Bibliographic record
Abstract
Achieving oriented and ordered remineralization on the surface of demineralized dental enamel, thereby restoring the satisfactory mechanical properties approaching those of sound enamel, is still a challenge for dentists. To mimic the natural biomineralization approach for enamel remineralization, the biological process of enamel development proteins, such as amelogenin, was simulated in this study. In this work, carboxymethyl chitosan (CMC) conjugated with alendronate (ALN) was applied to stabilize amorphous calcium phosphate (ACP) to form CMC/ACP nanoparticles. Sodium hypochlorite (NaClO) functioned as the protease which decompose amelogenin in vivo to degrade the CMC-ALN matrix and generate HAP@ACP core-shell nanoparticles. Finally, when guided by 10 mM glycine (Gly), HAP@ACP nanoparticles can arrange orderly and subsequently transform from an amorphous phase to well-ordered rod-like apatite crystals to achieve oriented and ordered biomimetic remineralization on acid-etched enamel surfaces. This biomimetic remineralization process is achieved through the oriented attachment (OA) of nanoparticles based on non-classical crystallization theory. These results indicate that finding and developing analogues of natural proteins such as amelogenin involved in the biomineralization by natural macromolecular polymers and imitating the process of biomineralization would be an effective strategy for enamel remineralization. Furthermore, this method represents a promising method for the management of early caries in minimal invasive dentistry (MID).
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".