Bioinspired caries preventive strategy via customizable pellicles of saliva-derived protein/peptide constructs
Bibliographic record
Abstract
Dental caries has been the most widespread chronic disease globally associated with significant health and financial burdens. Caries typically starts in the enamel, which is a unique tissue that cannot be healed or regrown; nonetheless, new preventive approaches have limitations and no effective care has developed yet. Since enamel is a non-renewable tissue, we believe that the intimate overlaying layer, the acquired enamel pellicle (AEP), plays a crucial lifetime protective role and could be employed to control bacterial adhesion and dental plaque succession. Based on our identified AEP whole proteome/peptidome, we investigated the bioinhibitory capacities of the native abundant proteins/peptides adsorbed in pellicle-mimicking conditions. Further, we designed novel hybrid constructs comprising antifouling and antimicrobial functional domains derived from statherin and histatin families, respectively, to attain synergistic preventive effects. Three novel constructs demonstrated significant multifaceted bio-inhibition compared to either the whole saliva and/or its native proteins/peptides via reducing biomass fouling and inducing biofilm dispersion beside triggering bacterial cell death. These data are valuable to bioengineer precision-guided enamel pellicles as an efficient and versatile prevention remedy. In conclusion, integrating complementary acting functional domains of salivary proteins/peptides is a novel translational approach to design multifunctional customizable enamel pellicles for caries prevention.
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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 | 0.000 | 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".