Regional variations in serum IL-35 levels and association with systemic lupus erythematosus: a systematic review and meta-analysis
Bibliographic record
Abstract
Interleukin (IL)-35 is an anti-inflammatory cytokine that regulates autoimmune diseases, including systemic lupus erythematosus (SLE). However, the association between the cytokine and disease may vary by geographical region. This study performed a meta-analysis to quantitatively assess the correlation between the serum IL-35 levels in SLE patients and sub-group analyses were conducted. Four main electronic databases-Scopus, Embase, Science Direct, PubMed-were searched for relevant studies. After a database search, Endnote software was used to find and remove duplicate studies. Random-effects models were used to estimate standard mean differences in serum/plasma IL-35 levels by Hedges' g with 95% confidence intervals (CIs). Publication bias was assessed with funnel plots, and risk of bias was assessed according to the Newcastle-Ottawa Scale (NOS). Sixteen studies met the eligibility criteria and were included in a qualitative review; data from 15 studies were included in the meta-analysis. Total IL-35 levels (pg/mL) did not differ among patients with active SLE and healthy controls (Hedges's g: 0.22, 95% CI - 0.51, 0.95, p = 0.55). Sub-group analysis revealed that IL-35 levels in patients with active SLE were lower than in healthy controls in Chinese studies (Hedges's g: - 3.11, 95% CI - 5.72, - 0.51), but not in non-Chinese studies (Hedges's g: 1.63, 95% CI - 0.31, 3.57). This regional difference was statistically significant (p < 0.01). The analysis comparing patients with inactive SLE and healthy controls showed a similar trend. This study suggests that serum IL-35 levels are lower in patients with SLE in studies from China, but not other regions. However, standardized protocols with large sample sizes are needed to confirm these findings.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".