Abundance of MMPs and Cysteine Cathepsins in Caries-affected Dentin
Bibliographic record
Abstract
Degradation of dentin matrix components within caries dentin has been correlated with the activity of host-derived proteases, such as matrix metalloproteases (MMPs) and cysteine cathepsins (CTs). Since this relationship has not been fully established, we hypothesized that the abundance of MMPs and CTs in caries-affected dentin must be higher than in intact dentin. To test this premise, we obtained 5 slices (200 µm) from 5 intact teeth and from 5 caries-affected teeth (1 slice/tooth) and individually incubated them with primary antibodies for CT-B, CT-K, MMP-2, or MMP-9. Negative controls were incubated with pre-immune serum. Specimens were washed and re-incubated with the respective fluorescent secondary antibody. Collagen identification, attained by the autofluorescence capture technique, and protease localization were evaluated by multi-photon confocal microscopy. The images were analyzed with ZEN software, which also quantitatively measured the percentages of collagen and protease distribution in dentin compartments. The abundance of the test enzymes was markedly higher in caries-affected than in intact dentin. CT-B exhibited the highest percentage of co-localization with collagen, followed by MMP-9, MMP-2, and CT-K. The high expression of CTs and MMPs in caries-affected teeth indicates that those host-derived enzymes are intensely involved with caries progression.
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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 | 0.000 |
| 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 | 0.000 |
| 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".