Cytokine polymorphisms in the Th1/Th2 pathway and susceptibility to non-Hodgkin lymphoma
Bibliographic record
Abstract
Studies have demonstrated that common polymorphisms in Th1 and Th2 cytokine genes can alter gene expression, modulate the balance between Th1/Th2 responsiveness, and influence susceptibility for autoimmune disorders, infectious diseases, and cancer. We analyzed one or more single nucleotide polymorphisms (SNPs) in 20 candidate Th1/Th2 genes in a population-based case-control study of non-Hodgkin lymphoma (NHL; n = 518 cases, 597 controls) among women in Connecticut. SNPs in critical genes, IL4, IL5, IL6, and IL10, were associated with risk for NHL and in some instances with a specific histologic subtype. Analysis of 4 SNPs in the IL10 promoter (-3575T>A, -1082A>G, -819C>T, and -592C>A) revealed that both the AGCC haplotype (odds ratio [OR] = 1.54, 95% confidence interval [CI] = 1.21-1.96, P < .001) and the TATA haplotype (OR = 1.37, 95% CI = 1.05-1.79, P = .02) were associated with increased risk for B-cell lymphomas. In contrast, the IL4-1098G allele was associated with increased risk of T-cell lymphomas (OR = 3.84; 95% CI = 1.79-8.22; P < .001). Further, the IL10 and IL4 SNP associations remained significant after adjusting for multiple comparisons. These results suggest that SNPs in Th2 cytokine genes may be associated with risk of NHL.
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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 | 0.001 |
| 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".