Interleukin‐1β and Interleukin‐6 Expression and Gene Polymorphisms in Subjects with Peri‐Implant Disease
Bibliographic record
Abstract
BACKGROUND: Polymorphisms found in the IL-1 family genes have been associated with susceptibility of periodontal disease. However, very little is known about the relationship between polymorphisms on inflammatory mediators' genes and peri-implant disease. PURPOSE: The aim of the present study was to evaluate interleukin-1β (IL-1β) and interleukin-6 (IL-6) concentration in the crevicular fluid, and the impact of gene polymorphisms on healthy and diseased implants in comparison with healthy teeth. MATERIALS AND METHODS: We examined 47 implants and teeth in 47 patients grouped as: 31 healthy implants, 16 implants with peri-implantitis, 31 healthy teeth from patients with healthy implants, and 16 healthy teeth from patients with peri-implantitis. Clinical parameters were recorded from all implants and teeth. Gingival crevicular fluid was collected to evaluate the concentration of IL-1β and IL-6. Cells from buccal mucosa were collected and their genomic DNA extracted for identification of the following polymorphisms: IL1B+3954, IL1B-511, and IL6-174. RESULTS: Clinical evaluation demonstrated that implants with peri-implantitis had less favorable indexes for probing depth (PD), relative clinical attachment level (CAL), bleeding on probing, and suppuration when compared with healthy implants and, for PD and CAL when compared with healthy teeth. There was no significant difference in the concentration of IL-1β and IL-6 detected between groups. There were no statistically significant differences between alleles and polymorphisms distribution on the studied population. CONCLUSIONS: There was no correlation between the concentration of IL-1β and IL-6 in the crevicular sulcular fluid present in healthy or diseased osseointegrated implants in comparison with healthy teeth. The studied gene polymorphisms had no influence on peri-implant disease.
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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 | 0.001 |
| 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) | 0.002 | 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".