Natural Killer-22 Cells in the Synovial Fluid of Patients with Rheumatoid Arthritis Are an Innate Source of Interleukin 22 and Tumor Necrosis Factor-α
Bibliographic record
Abstract
OBJECTIVE: To determine the role of natural killer (NK)-22 cells in the pathogenesis of rheumatoid arthritis (RA). METHODS: Using flow cytometry, the proportions of NK-22 cells and intracellular contents of perforin, granzyme B, and interferon-γ (IFN-γ) were determined in the peripheral blood (PB) and synovial fluid (SF) of patients with RA and healthy individuals. The levels of interleukin 22 (IL-22) and tumor necrosis factor-α (TNF-α) in the NK-22 supernatant and gene expressions were measured using ELISA and QuantiGene Plex assay, respectively. The effect of NK-22 supernatant on the proliferation of fibroblast-like synoviocytes (FLS) and recombinant human IL-22 (rhIL-22) on the production of monocyte chemoattractant protein 1 (MCP-1) by RA FLS was detected using the yellow tetrazolium salt method and ELISA, respectively. The relationship between the proportions of NK-22 cells and disease activity was analyzed. RESULTS: NKp44 and CCR6 were expressed in a larger population of SF NK cells than in the PB NK cells of patients with RA. NK-22 cells produce low content of perforin, granzyme B, and IFN-γ. NK-22 cells in vitro can secrete IL-22 and TNF-α and there was increased messenger RNA coding for IL-22 and TNF-α. NK-22 supernatant can induce the proliferation of RA FLS. Addition of IL-22 antibody plus TNF-α antibody inhibited the proliferation of FLS induced by the NK-22 supernatant. Both rhIL-22 1 ng/ml and rhIL-22 10 ng/ml induced the production of MCP-1 by RA FLS. The NK-22 proportions were positively correlated with disease activity. CONCLUSION: NK-22 cells are increased in patients with RA and might play a role in the pathogenesis of RA through the production of IL-22 and TNF-α. The proportion of NK-22 cells and disease activity were highly correlated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 0.001 |
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".