Follistatin-like Protein 1 and the Ferritin/Erythrocyte Sedimentation Rate Ratio Are Potential Biomarkers for Dysregulated Gene Expression and Macrophage Activation Syndrome in Systemic Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Follistatin-like protein 1 (FSTL-1) is a secreted glycoprotein overexpressed in certain inflammatory diseases. Our objective was to correlate FSTL-1 levels with gene expression, known biomarkers, and measures of disease activity in systemic juvenile idiopathic arthritis (sJIA), including macrophage activation syndrome (MAS). METHODS: FSTL-1 serum levels were measured by ELISA in 28 patients with sJIA, including 7 patients who developed MAS, and 30 healthy controls. Levels were correlated with erythrocyte sedimentation rate (ESR), ferritin, and soluble interleukin-2 receptor-α (sIL-2Rα). Gene expression based on FSTL-1 levels was analyzed in peripheral blood mononuclear cells (PBMC). RESULTS: Serum levels of FSTL-1 were elevated at time of presentation of sJIA (mean 200.7 ng/ml) and decreased to normal (mean 133.7 ng/ml) over 24 months (p < 0.01). FSTL-1 levels were markedly elevated during acute MAS (mean 279.8 ng/ml) and decreased to normal following treatment (p < 0.001). FSTL-1 levels correlated with serum markers of inflammation, including sIL-2Rα and ferritin. Ferritin/ESR ratio was superior to ferritin, sIL-2Rα, and FSTL-1 in discriminating MAS from new-onset sJIA. PBMC from patients with FSTL-1 levels > 200 ng/ml showed altered expression of genes related to innate immunity, erythropoiesis, and natural killer cell dysfunction. Two patients with the highest FSTL-1 levels at disease onset (> 300 ng/ml) ultimately developed MAS. CONCLUSION: Elevated pretreatment serum FSTL-1 levels in sJIA are associated with dysregulated gene expression suggestive of occult MAS, and may have utility in predicting progression to overt MAS. Ferritin/ESR ratio may be superior to ferritin alone in discriminating overt MAS from new-onset sJIA.
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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 | 0.000 |
| 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".