Circulating Dickkopf-1 Is Correlated with Bone Erosion and Inflammation in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To explore the potential role of Dickkopf-1 (DKK-1) in rheumatoid arthritis (RA) and to evaluate the effect of a tumor necrosis factor-α (TNF-α) inhibitor (infliximab) and an interleukin 1 receptor antagonist (IL-1Ra; anakinra) on DKK-1 secretion in patients with RA. METHODS: Serum samples were collected from 100 patients with RA, 100 patients with other rheumatic diseases (e.g., osteoarthritis and ankylosing spondylitis), and 40 healthy controls. DKK-1 and osteoprotegerin (OPG) levels in serum were detected by ELISA. Serum C-reactive protein (CRP) levels, erythrocyte sedimentation rates (ESR), rheumatoid factor (RF) titers, and anti-cyclic citrullinated peptide antibody were also measured in patients with RA. RESULTS: The serum level of DKK-1 was significantly higher in patients with RA than in healthy controls and those with other rheumatic diseases (p < 0.01); the serum DKK-1 level was correlated with levels of CRP (r = 0.488, p = 0.003) and ESR (r = 0.458, p = 2.4 x 10(-4)) and the Sharp score of radiologic change (r = 0.449, p = 0.001) in RA. In contrast to the increasing level of OPG, DKK-1 was significantly decreased in RA patients treated with TNF-α inhibitor (p < 0.01). DKK-1 was significantly decreased in RA patients treated with IL-1Ra (p < 0.01). CONCLUSION: DKK-1, as an important mediator, was correlated with bone erosion and inflammation in RA. The change of DKK-1 level may serve as a biomarker of disease activity and bone erosion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".