Genetic variants of interferon-gamma and its mRNA expression and inflammatory parameters in the pathogenesis of vitiligo
Bibliographic record
Abstract
Although genetics plays an essential role in the pathogenesis of vitiligo, vitiligo pathogenesis is still unclear. Our aim was to investigate the role of IFN-γ expression and polymorphism in vitiligo susceptibility and whether intercellular adhesion molecule-1 (ICAM-1), tumor necrosis factor (TNF)-α, and TNF-β play a role in vitiligo pathogenesis as important inflammatory parameters. Eighty-five patients with vitiligo and 90 controls were investigated for IFN-γ gene expression by quantitative real-time PCR and genotyped for IFN-γ +874T/A (rs2430561) and IFN-γ +2109A/G (rs1861494) gene polymorphisms by sequence-specific primer (SSP)-PCR and PCR-restriction fragment length polymorphism (RFLP), respectively. Serum levels of inflammatory parameters were measured using ELISA. Frequencies of the +874 TT genotype and T allele were significantly higher in patients with active vitiligo than in stable patients (P = 0.01 and 0.03, respectively). Calculation of odds ratio suggested a 1.7-fold increased risk of vitiligo in individuals having the TA haplotype. We observed overexpression of IFN-γ mRNA with elevated serum levels of IFN-γ, ICAM-1, TNF-α, and TNF-β in patients with vitiligo when compared with the control group (P = 0.001, for all). In addition, these levels were elevated in patients with active vitiligo compared with stable patients with vitiligo (P = 0.008, 0.006, 0.01, 0.01, and 0.03, respectively), which suggests the involvement of these cytokines in disease activity. In conclusion, IFN-γ is a promising immunological marker in vitiligo pathogenesis.
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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) | 0.001 | 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".