Tenascin‐C and Matrix Metalloproteinase‐9 Levels in Crevicular Fluid of Teeth and Implants
Bibliographic record
Abstract
BACKGROUND: The role of and interaction between bacterial infection and biomechanical impact in the development of peri-implant inflammatory processes is not clear. OBJECTIVE: To determine the amount and concentration of tenascin-C (TNC) in gingival crevicular fluid (GCF) around teeth and in peri-implant sulcus fluid from healthy implants and implants with peri-implantitis, and to correlate it with matrix metalloproteinase-9 (MMP-9) levels. MATERIALS AND METHODS: Seven control individuals and 18 patients with 41 implants with/without peri-implantitis were included. GCF was collected with filter strips and volumes were measured with a Periotron device. The amount of serum albumin per sample was quantified by densitometric analysis of Coomassie-stained sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Relative activity of MMP-9 was determined from the densitometry of zymograms. Amounts and concentrations of TNC were evaluated by ELISA. RESULTS: Relative MMP-9 activity was increased in peri-implantitis. A tendency was observed to measure higher TNC concentrations at teeth than at implants. The amount of TNC in GCF collected from healthy implant sites and the peri-implantitis sites was significantly different. Based on immunoblotting, TNC in GCF seemed degraded. In contrast to TNC, MMP-9 was significantly related to the PD and the volume of GCF. CONCLUSION: TNC is known to be induced in inflammation. The increase found in peri-implantitis was less than expected. In the context of peri-implantitis, TNC might be a marker of bone remodelling rather than inflammation and infection. A possible proteolytic degradation of TNC during peri-implantitis needs to be studied.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".