Surface APRIL Is Elevated on Myeloid Cells and Is Associated with Disease Activity in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To assess surface APRIL (a proliferation-inducing ligand; CD256) expression by circulating myeloid cells in rheumatoid arthritis (RA) and to determine its relationship to disease activity. METHODS: Peripheral blood mononuclear cells (PBMC) and plasma were obtained from patients with RA and healthy donors. PBMC were stained for flow cytometry to detect surface APRIL and blood cell markers to identify circulating myeloid cell subsets. Based on CD14 and CD16 phenotypes, monocyte subsets described as classical (CD14+CD16-), intermediate (CD14+CD16+), and nonclassical (CD14loCD16+) were identified. Levels of surface APRIL expression were measured by flow cytometry and median fluorescence intensity was used for comparisons. Levels of soluble APRIL in the plasma were determined by ELISA. Disease activity was measured by the Disease Activity Score in 28 joints. RESULTS: In patients with RA, total myeloid cells showed expression of surface APRIL that correlated with disease activity and with plasma APRIL levels observed in these patients. In healthy donors, classical monocytes were composed of > 80% of circulating monocytes. However, in patients with RA, the intermediate and nonclassical subsets were elevated and made up the majority of circulating monocytes. In contrast to healthy donors, where high levels of surface APRIL were only observed in nonclassical monocytes, patients with RA showed high levels of surface APRIL expression by all circulating monocyte subsets. CONCLUSION: Surface APRIL is elevated in circulating myeloid cells in patients with RA where it is highly correlated with disease activity. Patients with RA also showed skewing of monocytes toward subsets associated with secretion of tumor necrosis factor-α and/or interleukin 1β.
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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".