Expression of interleukin‐17 in primary Sjögren's syndrome and the correlation with disease severity: A systematic review and meta‐analysis
Bibliographic record
Abstract
The aberrant expression of interleukin-17 (IL-17) has been reported in primary Sjögren's syndrome (pSS). Abnormalities in IL-17 can promote the production of pro-inflammatory cytokines and aggravate autoimmune disorders. The aim of this study was to investigate alterations of IL-17 in patients with pSS and explore the correlation between IL-17 and disease severity. Eight databases were searched for original studies reporting the expression of IL-17 in patients with pSS and controls. Eligible reports were included in the pooled analysis, and subgroup evaluations were performed according to different types of controls and IL-17 measurement methods. Newcastle-Ottawa Scale criteria were used to assess the risk of bias of the included studies. In total, 45 articles are included in the meta-analysis. The expression of IL-17 is significantly increased in patients with pSS compared to controls. Furthermore, patients with pSS without immunosuppressive treatment show markedly higher IL-17 levels. In addition, patients with pSS with positive rheumatoid factors tend to express a higher level of IL-17 than patients with negative rheumatoid factors. Negative correlations between IL-17 levels and ocular parameters are also found in patients with pSS. The results are similar after adjustment by "trim and fill" methods. In conclusion, the expression of IL-17 is obviously increased in patients with pSS, especially among those without immunosuppressive treatment. In addition, IL-17 level correlates with the disease severity of pSS. These findings demonstrate the significance of IL-17 overexpression in patients with pSS and may provide insights for the development of therapeutic interventions targeting IL-17 for pSS.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".