The Potential Role of Interferon-regulatory Factor 7 Among Taiwanese Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Type I interferons (IFN), especially IFN-α, have been proposed to underlie the pathogenesis of systemic lupus erythematosus (SLE). Members of the IFN regulatory factor (IRF) family, which regulate IFN expression, have been implicated as risk factors for SLE. Our aims were to investigate the expression of IRF7 and its correlation with disease activity and to explore the association in Taiwanese patients between 2 genetic single-nucleotide polymorphisms (SNP) of IRF7 and SLE. METHODS: IRF7 messenger RNA (mRNA) levels were measured in peripheral blood mononuclear cells by real-time reverse transcription polymerase chain reaction in 51 adult patients with SLE and 65 age-matched and sex-matched controls. Their serum IFN-α levels were determined by ELISA and the clinical manifestations were recorded at the same time. Two IRF7 SNP, rs1061501 and rs1061502, were examined by genotyping across 92 patients with SLE and 92 age and sex-matched healthy control subjects. RESULTS: Compared with controls, the expression of IRF7 mRNA was significantly increased in patients with SLE and was positively correlated with both the serum level of IFN-α and lupus disease activity. The distribution of SNP rs1061501 by genotype (CC, CT, and TT) and by allele (C, T) was significantly different between the SLE and the control group (p = 0.028 for genotype and p = 0.009 for allele). There were no significant differences for SNP rs1061502. CONCLUSION: The results suggest that dysregulation of IRF7 might mediate an excessive production of IFN-α, which then exerts a crucial effect on the pathogenesis of human SLE. The IRF7 SNP rs1061501 TT genotype and T allele are enriched in Taiwanese patients with SLE and thus would seem to be associated with an increased risk of developing SLE.
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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 | 0.001 |
| 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".