The Role of Tumor Necrosis Factor-α Promoter Genetic Variation in Takayasu Arteritis Susceptibility and Medical Treatment
Bibliographic record
Abstract
OBJECTIVE: Tumor necrosis factor-α (TNF-α) is a multifunctional proinflammatory cytokine that influences the pathogenesis of Takayasu arteritis (TA). There is still no evidence of the relationship between TNF-α gene promoter polymorphisms and TA. We examined whether variations in the TNF-α promoter region may lead to TA susceptibility and disease progression. METHODS: Five TNF-α gene promoter polymorphisms (-238G/A, -308G/A, -857C/T, -863C/A, and -1031C/T) were analyzed in 110 Chinese Han patients with TA, with a control group of 362 unrelated healthy individuals. Genotypes of TNF-α gene promoter polymorphisms were identified by direct sequencing. TNF-α plasma concentrations were determined by ELISA. RESULTS: Our results indicated that the frequency of the -863A allele was significantly lower in the patients with TA than in the controls (18.2% vs 25.7%; p = 0.011), but the significance was lost after Bonferroni correction (p(c) = 0.055). The frequency of -863CA/AA genotypes was significantly lower in the patients with refractory TA than in those with the 863CC genotype (22.4% vs 44.2%; p(c) < 0.01). The frequency of the GGCCT haplotype was significantly higher in patients than in the controls, while the frequencies of GGCAT and GGCCC haplotypes were significantly lower in patients than in controls. The plasma TNF-α concentrations were significantly lower in the subjects carrying the -863A allele than in those without. Patients with active TA had a significant increase in plasma levels of TNF-α compared with remission patients and the control group. CONCLUSION: Polymorphisms of the TNF-α promoter are not associated with TA in the Chinese Han population. The A allele of the -863C/A polymorphism is associated with decreased TNF-α expression, which might affect medical treatment.
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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 | 0.002 |
| 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".