TNFA and IL10 Gene Polymorphisms are not Associated with Periodontitis in Brazilians
Bibliographic record
Abstract
IL-10 and TNF-α are cytokines that have complex and opposing roles in the inflammatory responses. G/A polymorphisms at position -1082 of IL10 and -308 of TNFA genes have been reported to influence the expression of IL-10 and TNF-α, respectively. The aim of this study was to investigate the association between the IL10 (-1082) and TNFA (- 308) gene polymorphisms with different clinical forms or severity of periodontitis in a sample of Brazilian individuals. DNA was obtained from oral swabs of 165 Brazilian individuals, which were divided into three groups: individuals with chronic periodontitis, aggressive periodontitis and individuals without clinical evidence of periodontitis. Evaluation of IL10 and TNFA polymorphisms was performed by RFLP analysis. Statistical analysis of data was performed using the Χ² likelihood ratio and Fisher;s exact test. No significant differences in the genotype and allele distribution of either IL10 or TNFA were observed among individuals with different clinical forms or with different degrees of severity of periodontitis. Moreover, combined analysis of IL10 and TNFA polymorphisms did not show any association with periodontal status. As conclusion, the IL10 and TNFA gene promoter polymorphisms investigated are not associated with different clinical forms of periodontitis or with severity of the disease in the Brazilian population polymorphisms.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".