Intracytoplasmic Cytokine Expression and T Cell Subset Distribution in the Peripheral Blood of Patients with Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: To determine the role of inflammatory mediators in the pathogenesis of ankylosing spondylitis (AS), we investigated peripheral blood lymphocyte subsets and their intracellular cytokine production. METHODS: The percentages of T and B lymphocytes, natural killer (NK) cells, activated T lymphocytes, CD4+ T helper (Th), and CD8+ T cytotoxic (Tc) cells were determined by flow cytometry in 42 patients with AS compared to 52 healthy controls. In order to assess circulating Th1/Th2 and Tc1/Tc2 subsets, we used a whole-blood cytometric assay based on the intracellular interferon-gamma, interleukin 4 (IL-4), and IL-10 expression of the cells. RESULTS: In the peripheral blood, the frequencies of CD4+ T helper and CD56+ NK cells were higher in AS (54.8% and 16.2%, respectively) compared to controls (45.3% and 10.8%) (p < 0.05). The frequencies of Th0 (1.9% vs 0.8%) and Tc0 (2.1% vs 0.8%) cells were higher, while that of Tc1 cells was lower (26.6% vs 40.1%) in patients with AS versus controls (p < 0.05). The percentage of IL-10-producing Tc cells was significantly higher in AS (18.4%) versus controls (8.5%) (p < 0.05). Finally, the active phase of AS was associated with significantly lower percentage of IL-10-producing Tc cells in the peripheral blood (6.6%) compared to patients with inactive AS (23.1%). CONCLUSION: Our results provide further evidence for an altered T cell subset distribution and intracytoplasmic cytokine balance in AS.
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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".