Effects of nonsteroidal anti-inflammatory drugs on serum proinflammatory cytokines in the treatment of ankylosing spondylitis
Bibliographic record
Abstract
OBJECTIVE: This study was conducted to investigate the correlation between serum levels of proinflammatory cytokines and the clinical efficacy of nonsteroidal anti-inflammatory drugs (NSAIDs) in patients with ankylosing spondylitis (AS). METHODS: A total of 148 patients with AS were selected and received NSAID treatment. ELISA was used to assess cytokine levels, and patients were assigned into the following groups: positively effective; effective; moderately effective; and ineffective. Spearman and Pearson correlation analyses were used for correlation analysis. RESULTS: The erythrocyte sedimentation rates (ESR), C-reactive protein (CRP) levels, and immunoglobulin A (IgA) levels of the case group after NSAID treatment were markedly lower than those before NSAID treatment. After treatment, the levels of interleukin (IL)-6, IL-17, and tumor necrosis factor (TNF)-α were markedly reduced, while IL-10 levels increased in the positively effective, effective, and moderately effective groups, and IL-12 levels decreased in the positively effective and effective groups. In addition, the levels of IL-6 and TNF-α were correlated with a greater number in the efficacy indexes and clinical parameters, followed by IL-10 levels, while the levels of IL-17 and IL-12 had relatively weaker correlations with these indexes and parameters. CONCLUSION: NSAIDs could promote the clinical efficacy of treatment for ankylosing spondylitis by regulating serum levels of proinflammatory 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".