Interleukin‐2, ‐16, and ‐17 gene polymorphisms in Iranian patients with chronic periodontitis
Bibliographic record
Abstract
Abstract Aim Chronic periodontitis (CP) is a multifactorial disease and the most common type of periodontitis mainly caused by microbial plaque. CP can be brought on by, and progresses with, insufficient oral hygiene, and environmental and genetic susceptibilities. The aim of the present study was to investigate the association between interleukin (IL)‐2 (T‐330G), IL‐16 (T‐295C), and IL‐17 (A‐7383G) gene polymorphisms and the susceptibility to CP in an Iranian population. Methods Ninety‐nine cases diagnosed with CP and 75 matched healthy controls engaged in the present study. 3 cc peripheral blood samples were obtained for DNA isolation. Genotype analysis was performed using restriction fragment length polymorphism polymerase chain reaction. Genotype distribution and allele frequencies within groups were compared using χ2‐test, and logistic regression analysis was used to recognize the independent relation between the disease and the absence or presence of alleles. Results There was no polymorphism in IL‐2 (T‐330G) among our patients, and the TT genotype was present in both study groups. Moreover, none of the studied genotypes and alleles of IL‐16 (T‐295C) and IL‐17 (A‐7383G) was significantly associated with CP. Conclusion The present study demonstrated no association between IL‐2 (T‐330G), IL‐16 (T‐295C), and IL‐17 (A‐7383G) genotypes and CP in an Iranian population.
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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) | 0.001 | 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".