Association between TNF-α, TGF-β1, IL-10, IL-6 and IFN-γ gene polymorphisms and generalized aggressive periodontitis
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate links among cytokine genetic variants and generalized aggressive periodontitis (GAgP). METHODS: Thirty-five patients with generalized aggressive periodontitis and 85 healthy controls without periodontitis were included in the study. Probing depth (PD), clinical attachment loss (CAL), plaque index (PI), and gingival index (GI) were recorded as clinical parameters. Polymorphisms of IL-6, IL-10, IFN-gamma, TGF-ss1 and TNF-alpha gene were analysed using the polymerase chain reaction sequence-specific primer method (PCR-SSP). RESULTS: No significant differences were observed for IL-6, IL-10, IFN-gamma, and TGF-ss1 cytokine polymorphisms, from the genotype distribution and allele frequency, between GAgP and healthy control groups. In contrast, significant differences were observed in the TNF-alpha gene polymorphism between GAgP and healthy control groups (P = 0.002). CONCLUSION: Our data suggest that TNF-alpha (-308) may be associated with the development of generalized aggressive periodontitis. These results should be replicated in a larger and more diverse population of patients diagnosed with generalized aggressive periodontitis to determine of these findings are generalizable.
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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 | 0.000 | 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) | 0.002 | 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".