Tocilizumab Treatment Decreases Circulating Myeloid Dendritic Cells and Monocytes, 2 Components of the Myeloid Lineage
Bibliographic record
Abstract
OBJECTIVE: Interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α) are proinflammatory cytokines involved in inflammatory response. Effective TNF-α blocker treatment is associated with an increase in circulating myeloid dendritic cells (mDC), suggesting their release from inflamed synovium. Currently, in vivo effects of IL-6 inhibition on DC are unknown. We monitored the changes in circulating mDC and plasmacytoid DC (pDC) during tocilizumab (TCZ) therapy in patients with rheumatoid arthritis (RA). METHODS: DC subset levels were evaluated by flow cytometry in patients with RA (n = 43) and in healthy volunteers (n = 20). In patients with RA, these levels were measured before and during TCZ therapy (8 mg/kg every 4 weeks). Response to TCZ therapy was evaluated at 12 weeks. Statistical analysis was based on Mann-Whitney U tests or Wilcoxon signed-rank tests. RESULTS: At baseline, patients with active RA were characterized by a significantly lower level of circulating mDC and pDC compared to healthy donors. However, this difference did not correlate with any disease activity score. TCZ-treated patients who met the European League Against Rheumatism (EULAR) improvement criteria at Week 12 had significant reductions in mDC and monocyte levels as compared with EULAR nonresponders. Levels of pDC, CD4+ T cells, and CD8+ T cells remained stable during the TCZ courses, regardless of treatment response. CONCLUSION: Our study reveals an unexpected reduction of circulating mDC and monocytes in patients with RA in response to TCZ therapy. In accord with reports on neutrophils and platelets decreasing during TCZ therapy, our data suggest an effect of IL-6 inhibition on cells from myeloid lineage.
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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 | 0.000 |
| 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) | 0.002 | 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".