Increased serum levels of TGFβ1 in children with localized scleroderma
Bibliographic record
Abstract
BACKGROUND: There are neither sensitive nor specific laboratory tests for measuring disease activity in localized scleroderma (LS). Monitoring is done almost exclusively by clinical assessment. Our aim was to determine whether serum concentrations of TGFbeta1 are a good biomarker of disease activity in children with LS. METHODS: 55 pediatric patients with LS were divided into sub-types according to their main lesion; morphea, generalized morphea, linear scleoderma affecting a limb or the face. The lesions were further categorized by overall clinical assessment into active, inactive, and indeterminate groups according to disease activity. Serum TGFbeta1 concentration levels were measured by enzyme linked immunosorbent assay (ELISA), analyzed and correlated with disease subtypes and disease activity. RESULTS: The mean TGFbeta1 concentration were significantly higher in the patient group (51393 +/- 33953 pg/ml) than in the control group (9825 +/- 5287 pg/ml) (P < 0.001). The mean concentration were elevated in all the disease subtypes, and did not correlate with disease duration or activity. CONCLUSION: Serum concentration of TGFbeta1 were elevated in patients with all subtypes of LS irrespective of clinical disease activity. Although TGFbeta1 may play an important role in the pathogenesis of local skin fibrosis, circulating blood levels of molecules known to act locally may not be useful biomarkers of disease activity.
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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 | 0.001 | 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) | 0.001 | 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".