Synovial Biomarkers in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To find candidate biomarkers of psoriatic arthritis (PsA). A panel of synovial fluid (SF) and synovial tissue (ST) biomarkers was analyzed in patients with resistant peripheral PsA, in relation to clinical and imaging outcomes of synovitis response following serial intraarticular (IA) etanercept injections (12.5 mg). METHODS: Fourteen PsA patients with resistant knee joint synovitis were treated with 4 IA etanercept injections in a single knee joint, once every 2 weeks. Primary outcome (Thompson's knee index: THOMP) and secondary outcomes were assessed at baseline and end of study: C-reactive protein, Knee Joint Articular Index (KJAI), Health Assessment Questionnaire disability index, maximal synovial thickness (MST) by gray-scale ultrasonography, contrast-enhanced magnetic resonance imaging (C+MRI), ST-cluster differentiation (CD)45+ mononuclear cell, ST-CD31+ vessels, and ST-CD105+ angiogenic endothelial cells, along with levels of SF interleukin 1ß (IL-1ß), IL-1 receptor antagonist (Ra), and IL-6. RESULTS: At the end of the study, clinical and imaging outcomes, ST and SF biological markers were significantly reduced compared to baseline. There was a significant association between IL-6 and either THOMP or KJAI; between either ST-CD31+ or ST-CD105+ or ST-CD45+; between ST and SF biomarkers expression (CD45+ and IL-1ß) and between ST-CD45+ and both KJAI and MRI-MST. Comparing pre- versus post-IA etanercept injection changes (Δ), Δ IL-1ß was significantly correlated with both Δ IL-6 and with Δ IL-1Ra and Δ IL-6 with Δ IL-1Ra. CONCLUSION: The association to disease activity and the changes following IA treatment indicate that ST-CD45+ and ST-CD31+, along with SF-IL-6 and SF-IL-1ß, may represent candidate biomarkers of the knee synovitis response to IA tumor necrosis factor-α blockade.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".