Identification of acid-resistant proteins in acquired enamel pellicle
Bibliographic record
Abstract
OBJECTIVES: This study characterized the proteome profile of the acquired pellicle formed in vivo on enamel. Changes in this proteome profile after exposure to lactic or citric acid were also evaluated. METHODS: Volunteers (n=8) were subjected to dental prophylaxis. After 2 h to allow the formation of the acquired pellicle, the teeth were isolated with cotton rolls and 1 mL of citric acid (1%, pH 2.5) or lactic acid (0.1 M pH 4.8) or deionized water was gently applied with a pipette on the anterior teeth (both maxillary and mandibular) for 10 s. In sequence, the pellicle was collected with an electrode filter paper soaked in 3% citric acid. This procedure was repeated for two additional days following a crossover protocol. Proteins were subjected to reverse phase liquid chromatography coupled to mass spectrometry (nLC-ESI-MS/MS). MS/MS data were processed and submitted to Proteome Discoverer software. Searches were done using SWISS-PROT and TrEMBL databases for human proteins. RESULTS: In total, seventy-two proteins were present in all groups and were submitted to quantitative analysis (SIEVE). Some of these proteins were increased more than two-fold after exposure to the acids. Among them, cystatin-B was increased 20- and 13-fold after exposure to citric and lactic acids, respectively. Additionally, some proteins were identified in only one of the groups (18, 5, and 11 proteins for deionized water, citric and lactic acids, respectively). CONCLUSIONS: Our results open new insights regarding potentially acid-resistant proteins that could be added to dental products to prevent acidic dissolution of the teeth.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".