Association between the Interleukin-1 Receptor Antagonist (IL1RN) Variable Number of Tandem Repeats (VNTR) Polymorphism and Lymphoma
Bibliographic record
Abstract
Introduction: Lymphoma is a common hematopoietic cancer. Immunosuppression is one of the main risk factors for the development of lymphoma. The interleukin (IL)-1 receptor antagonist IL1RN, which binds to the IL-1 receptor, moderates a variety of immune responses related to IL-1. We aimed to assess the impact of IL1RN variable number of tandem repeats (VNTR) polymorphism on lymphoma risk in an Iranian population sample. Materials and Methods: DNA was extracted from peripheral blood of 120 subjects with non-Hodgkin Lymphoma (NHL), 50 subjects with Hodgkin’s lymphoma (HL), and 186 unrelated healthy individuals. IL1RN VNTR polymorphism was detected using polymerase chain reaction. Results: Our findings revealed that the IL1RN VNTR polymorphism was associated with protection against NHL (P≤0.001, OR: 0.30, 95% CI: 0.18-0.53). The IL1RN 2 allele significantly decreased the risk of NHL (p = 0.023, OR = 0.66, 95%CI = 0.46–0.93). In addition, we found that IL1RN 1/2 was associated with a lower risk of HL (p ≤0.001, OR = 0.24, 95%CI = 0.12–0.50). Conclusion: Our results suggest that the presence of IL1RN VNTR polymorphism is associated with a decreased risk of lymphoma in an Iranian subpopulation in southeast Iran.
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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) | 0.003 | 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".