Characteristics of Circulating Natural Killer Cells and Their Interferon-γ Production in Active Adult-onset Still Disease
Bibliographic record
Abstract
Objective. To investigate the characteristics of circulating natural killer (NK) cells and their interferon (IFN)-γ–producing ability in adult-onset Still disease (AOSD). Methods. Peripheral blood mononuclear cells were obtained from 22 patients in the acute phase of AOSD (acute AOSD); 7 of the 22 patients after treatment (remission AOSD), and 11 healthy controls (HC). NK cells and their IFN-γ expression levels were analyzed by flow cytometry. Additionally, the cytokine receptors of interleukin (IL)-12, IL-15, and IL-18 on NK cells were also evaluated. Results. The frequency of NK cells was significantly lower in acute AOSD than in HC. NK cell counts significantly increased in remission AOSD. Expression of IL-12 and IL-15 receptors on NK cells was significantly increased in acute AOSD, whereas that of IL-18 receptor indicated no significant difference among 3 groups. IFN-γ expression in NK cells was significantly higher in acute AOSD than in HC, and significantly decreased in remission AOSD. The absolute number of NK cells and IFN-γ–expressing NK cells revealed an inverse correlation with serum ferritin levels in acute AOSD. In 2 distinct subsets of NK cells, CD56 dim NK cells significantly exhibited higher IFN-γ expression than CD56 bright NK cells in acute AOSD. Conclusion. In acute AOSD, NK cells displayed lower proportion, whereas they had higher ability for IFN-γ production than in HC; moreover, upregulation of IL-12 and IL-15 receptors on NK cells may promote IFN-γ production. In addition, disease activity may be implicated in regulating the number of NK cells and IFN-γ–expressing NK cells in AOSD.
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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) | 0.001 | 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".