<i>In Vivo</i> Peripheral Blood Proinflammatory T Cells in Patients with Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: Previous reports have shown an increase in peripheral blood mononuclear cells' (PBMC) Th17 cell subpopulation and tumor necrosis factor-α (TNF-α) secretion after in vitro stimulation with anti-CD3/CD28 or phorbol myristate acetate/ionomycin in ankylosing spondylitis (AS). The aim of our study was to determine whether there is a Th17 polarization not subjected to in vitro stimulation in patients with AS. METHODS: Nonstimulated PBMC were analyzed from 46 patients with AS, including 7 (15.2%) receiving tumor necrosis factor-α (TNF-α) inhibitors, 20 patients with rheumatoid arthritis, and 25 healthy controls. The surface phenotype of freshly isolated PBMC was determined by flow cytometry. Th1, Th2, Th17, and Treg subsets were defined as CD3+CD4+IFN-γ+, CD3+CD4+IL-4+, CD3+CD4+IL-17A+, and CD3+CD4+FoxP3+, respectively. Serum cytokines and interleukin 8 (IL-8) levels were quantified by Luminex technology. RESULTS: The percentages of Th17 and Th1 cells in AS were higher than in healthy controls (7.4% ± 1.8% vs 0.7% ± 0.2% and 4.0% ± 1.3% vs 1.1% ± 0.3%, respectively; p < 0.0001). Th17 and Th1 cell subsets in patients taking TNF-α inhibitors were lower than in those naive to such therapeutics and similar to healthy controls. Serum levels of IL-6, IL-17A, TNF-α, and IL-8 were significantly higher in patients with AS compared to controls. CONCLUSION: The percentages of Th17 and Th1 cells in PBMC without in vitro stimulation, as well as cytokine and IL-8 levels, were significantly increased in patients with AS compared with healthy controls. These T cell subsets and cytokine profiles of patients with AS taking TNF-α inhibitors were similar to those of healthy controls.
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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 | 0.001 | 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".