Nanoscale Adhesion Forces between Enamel Pellicle Proteins and Hydroxyapatite
Bibliographic record
Abstract
The acquired enamel pellicle (AEP) is important for minimizing the abrasion caused by parafunctional conditions as they occur, for instance, during bruxism. It is a remarkable feature of the AEP that a protein/peptide film can provide enough protection in normofunction to prevent teeth from abrasion and wear. Despite its obvious critical role in the protection of tooth surfaces, the essential adhesion features of AEP proteins on the enamel surface are poorly characterized. The objective of this study was to measure the adhesion force between histatin 5, a primary AEP component, and hydroxyapatite (HA) surfaces. Both biotinylated histatin 5 and biotinylated human serum albumin were allowed to adsorb to streptavidin-coated silica microspheres attached to atomic force microscope (AFM) cantilevers. A multimode AFM with a Nanoscope IIIa controller was used to measure the adhesion force between protein-functionalized silica microspheres attached to cantilever tips and the HA surface. The imaging was performed in tapping mode with a Si3N4 AFM cantilever, while the adhesion forces were measured in AFM contact mode. A collection of force-distance curves (~3,000/replicate) was obtained to generate histograms from which the adhesion forces between histatin 5 or albumin and the HA surface were measured. We found that histatin 5 exhibited stronger adhesion forces (90% >1.830 nN) to the HA surface than did albumin (90% > 0.282 nN). This study presents an objective approach to adhesion force measurements between histatin 5 and HA, and provides the experimental basis for measuring the same parameters for other AEP constituents. Such knowledge will help in the design of synthetic proteins and peptides with preventive and therapeutic benefits for tooth enamel.
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".