CXCL 9 and CXCL 10 as Sensitive Markers of Disease Activity in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To assess whether serum levels of CC and CXC chemokines correlate with disease activity in patients with rheumatoid arthritis (RA), and to determine whether these effects predict clinical response. METHODS: Serum levels of the chemokines CC (CCL2, CCL5) and CXC (CXCL8, CXCL9, CXCL10) were quantified at baseline and after 12 weeks of treatment with disease-modifying antirheumatic drugs or biologic agents in 28 patients using flow cytometry. Serum from 40 healthy individuals was collected for comparison at baseline. Response to treatment was classified according to the European League Against Rheumatism (EULAR) response criteria. Remission of disease was defined as a Disease Activity Score < 2.6. RESULTS: The baseline serum concentrations of CC and CXC chemokines were significantly elevated in patients with active RA compared to healthy controls (p < 0.05) except for CCL2. Significant improvement in all disease activity measurements was observed after 12 weeks of treatment. Seventeen (60.7%) patients achieved good to moderate response based on the EULAR response criteria, and 5 (17.9%) patients achieved remission. The improvement in clinical activity in patients with RA was accompanied by a significant reduction in the serum concentration of CXCL9 and CXCL10 (p < 0.001). A significant reduction in the serum level of CXCL10 was also observed in the group that achieved EULAR response. Serum concentration of CCL5 remained significantly elevated in patients with RA (n = 5) who achieved remission compared to the healthy controls (p < 0.05). CONCLUSION: Serum concentration of CXCL9 and CXCL10 may serve as sensitive biomarkers for disease activity in patients with RA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".