Analysis of <i>In Situ</i> Protease Activity in Chronic Adult Periodontitis Patients: Expression of Activated MMP‐2 and a 40 kDa Serine Protease
Bibliographic record
Abstract
BACKGROUND: Periodontitis is characterized by extensive destruction of the gingival tissues and associated supporting structures of the teeth. Although the pathogenesis of the various forms of this disease is not completely understood, host-derived proteases are believed to have an important role. In this study, we analyzed human tissue samples from chronic adult periodontitis patients to assess the levels of specific proteases and determine the effect of pH and tetracyclines on their activity. METHODS: Gingival tissue samples were obtained from patients with chronic adult periodontitis (probing depths ranged from 5 to 9 mm) and periodontally healthy controls. Tissue extracts were prepared and analyzed for protease activity by zymography and Western blotting. RESULTS: Maximal protease activity from clinically normal and diseased tissues was observed at pH 8. Latent matrix metalloproteinase (MMP)-9 and MMP-2 were expressed in all samples examined, while active MMP-2 was detected only in tissues obtained from patients with clinical disease. The MMP activities were differentially inhibited by derivatives of tetracycline. At pH 6, a protease with a mass of approximately 40 kDa was observed in diseased samples. The enzymatic activity was inhibited by phenylmethylsulfonyl fluoride, suggesting it is a serine protease. CONCLUSIONS: The results of the current study substantiate the proposed role of host-derived proteases in the pathogenesis of chronic adult periodontitis. Specifically, they indicate that activated MMP-2 and a 40 kDa serine protease are involved in tissue destruction associated with this form of periodontal disease and also suggest that tissue pH influences protease activity in situ.
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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) | 0.001 | 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".