Tumor Necrosis Factor-α −308 Genotypes Influence Inflammatory Activity and TNF-α Serum Concentrations in Children with Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Considering the relevance of tumor necrosis factor-alpha (TNF-alpha) in the pathophysiology of juvenile idiopathic arthritis (JIA), it is likely that polymorphisms in its promoter area may be relevant in disease susceptibility and activity. We investigated if clinical measures of JIA activity and TNF-alpha serum concentrations were associated with TNF-alpha -308 genotypes. METHODS: Portuguese patients with JIA in 5 pediatric rheumatology centers were recruited consecutively, along with a control group of healthy subjects. Demographic and clinical data and blood samples were collected from each patient. DNA was extracted for analysis of TNF-alpha gene promoter polymorphisms at position -308 by restriction fragment-length polymorphism. RESULTS: One hundred fourteen patients and 117 controls were evaluated; 57% of patients presented the oligoarticular subtype, 25% the polyarticular subtype, 8% the systemic subtype, and 9% had enthesitis-related arthritis and 5% psoriatic arthritis. Twenty-four percent of the patients presented the -308 GA/AA genotypes and 76% the -308 GG genotype, similar to findings in controls. Patients with the -308 GA/AA genotype had higher degree of functional impairment, erythrocyte sedimentation rate, 100-mm visual analog scale score for disease activity, and TNF-alpha levels compared to those with the -308 GG genotype. CONCLUSION: TNF-alpha -308 GA/AA genotypes were found to be related to higher inflammatory activity and worse measures of disease activity in Portuguese patients with JIA. They were not associated with susceptibility to JIA.
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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 | 0.001 |
| 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".