The Relation of Interleukin 17 (IL-17) and IL-23 to Th1/Th2 Cytokines and Disease Activity in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Interleukin 17 (IL-17) was recently linked to pathogenesis of systemic lupus erythematosus (SLE), but its relation to disease activity has not been well characterized. We examined the relation between serum levels of Th17 (IL-17, IL-23), Th1 (IL-12, interferon-γ), Th2 (IL-10, IL-6, IL-4) cytokines and disease activity in patients with SLE. METHODS: Serum cytokines were measured by enzyme linked immunosorbent assays. Disease activity was determined by SLE disease activity index (SLEDAI), anti-dsDNA antibody, and C3 and C4 levels. RESULTS: Serum levels of IL-17 (p < 0.001), IL-6 (p = 0.006) and IL-10 (p < 0.001) were higher in SLE patients (n = 70) compared to healthy controls (n = 36). Higher serum IL-23 level was found in patients with active disease with cutaneous manifestations (p = 0.004) and serositis (p = 0.04) compared to those without. Serum IL-17 level above the detection limit was more frequently found in patients who had active lupus nephritis (11/23, 47.8%) (p = 0.002), nonrenal active disease (9/15, 60%) (p = 0.001), and inactive lupus (21/32, 65.6%) (p < 0.001) compared to healthy controls (0%). Serum IL-17 levels were otherwise comparable between these 3 groups of patients and were not related to SLEDAI, glomerular filtration rate, activity or chronicity score and ISN/RPS criteria class among patients with active lupus nephritis. There was no significant correlation between serum IL-17/IL-23 and Th1 or Th2 cytokine levels. CONCLUSION: SLE patients had higher serum IL-17 levels than healthy controls. Elevated serum IL-23 was found in patients with inflammatory manifestations including cutaneous involvement and serositis. The lack of correlation between Th17, Th1, and Th2 cytokines suggested independent regulatory mechanisms for these cytokines.
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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 | 0.001 | 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".