Expression of the myeloid inhibitory receptor CLEC12A correlates with disease activity and cytokines in early rheumatoid arthritis
Bibliographic record
Abstract
The myeloid inhibitory receptor CLEC12A negatively regulates inflammation. Reduced CLEC12A expression enhances inflammation in CLEC12A knock-out mice with collagen antibody-induced arthritis. Moreover, CLEC12A internalisation augments human neutrophil activation. We thus postulated that CLEC12A expression on circulating myeloid cells of rheumatoid arthritis patients is associated with disease manifestations. Cell-surface, CLEC12A receptor expression was determined on circulating neutrophils and monocytes of eRA patients and of healthy donors. Generalized estimating equations model, Student's t-test and Spearman's correlations were performed to compare CLEC12A expression between groups and test its association with disease activity and clinical parameters. Plasma cytokines were measured by multiplex immunoassay. Patients with reduced neutrophil or monocyte CLEC12A expression at baseline and at 3 months have an increased simple disease activity index. Low baseline CLEC12A expression also correlates with a higher SDAI at 6 months. In contrast, positive correlations were observed between baseline CLEC12A expression and several cytokines. Moreover, neutrophil and monocyte CLEC12A expression is significantly higher in early rheumatoid arthritis patients at baseline than healthy controls. Circulating neutrophil and monocyte CLEC12A expression correlates with disease activity at baseline and is predictive of SDAI at later stages of the disease indicative of a regulatory role for CLEC12A in 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".