Elevated Circulating TWEAK Levels in Systemic Sclerosis: Association with Lower Frequency of Pulmonary Fibrosis
Bibliographic record
Abstract
OBJECTIVE: To determine serum levels of tumor necrosis factor-related weak inducer of apoptosis (TWEAK) and its clinical associations in patients with systemic sclerosis (SSc). METHODS: Serum TWEAK levels from 70 patients with SSc were examined by ELISA. In a retrospective longitudinal study, sera from 23 patients with SSc were analyzed (followup 0.8-7.2 yrs). RESULTS: Serum TWEAK levels were elevated in patients with SSc (n = 70) compared with healthy controls (n = 31) and patients with systemic lupus erythematosus (n = 22). Among patients with SSc, there were no differences in serum TWEAK levels between limited cutaneous SSc and diffuse cutaneous SSc. Patients with SSc who had elevated TWEAK levels less often had pulmonary fibrosis and decreased vital capacity than those with normal TWEAK levels. In the longitudinal study, SSc patients with inactive pulmonary fibrosis or without pulmonary fibrosis consistently exhibited increased TWEAK levels, while those with active pulmonary fibrosis showed decreased TWEAK levels during the followup period. CONCLUSION: TWEAK levels were increased in patients with SSc, and associated with a lower frequency of pulmonary fibrosis in patients with SSc. TWEAK could be a protective factor against the development of pulmonary fibrosis in this disease and as such would be a possible therapeutic target.
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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) | 0.002 | 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".