The Profile of Inflammatory Cytokines in Gingival Crevicular Fluid around Healthy Osseointegrated Implants
Bibliographic record
Abstract
OBJECTIVE: Regardless of gingival health and subgingival microbiology, production of cytokines within peri-implant tissues may be different from that of teeth. The objective of this study was to describe the peri-implant levels of pro-inflammatory cytokines and subgingival microbiology in clinically healthy sites. MATERIALS AND METHODS: Subgingival plaque and gingival crevicular fluid (GCF) were obtained from 28 clinically healthy implants and 26 teeth selected from 24 individuals. Microbial composition was determined by selective anaerobic culture techniques. Pro-inflammatory cytokines were quantified by flow cytometry analysis of GCF. The concentration of cytokines between implants and teeth were compared with the independent t-test. RESULTS: The concentration of cytokines was higher in GCF from healthy implants than in teeth. The profile of cytokines was characteristic of an innate immune response. A more frequent detection of periodontopathic bacteria was observed in teeth than implants. Cultivable levels of periodontopathic bacteria were similar between implants and teeth. CONCLUSIONS: Despite gingival tissue health and scarce plaque accumulation, the profile of inflammatory cytokines in implant crevicular fluid was distinctive of an innate immune response and in higher concentration than in teeth. Other than bacterial stimulus, intrinsic factors related to implants may account for more cytokine production than 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".