Tumor Necrosis Factor-α Blockade Leads to Decreased Peripheral T Cell Reactivity and Increased Dendritic Cell Number in Peripheral Blood of Patients with Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: To study the effect of tumor necrosis factor-alpha (TNF-alpha) antagonist (etanercept) treatment on the peripheral T cell reactivity of patients with ankylosing spondylitis (AS). METHODS: Peripheral blood mononuclear cells were collected from 40 patients with AS at baseline, after 2 and 6 weeks of etanercept treatment or placebo treatment, and from healthy controls. The number of cells secreting various cytokines was detected by enzyme linked immunospot. Serum soluble interleukin 2 (IL-2) receptor level was measured by ELISA. T cell proliferation was assayed with the WST-1 live cell-staining method. The myeloid dendritic cell (mDC) and regulatory T cell (Treg) levels were analyzed by fluorescence activated cell sorting. RESULTS: . After 2 and 6 weeks of etanercept treatment, the number of TNF-alpha-secreting monocytes decreased. Although the T cell proliferation rate remained stable, the number of T cells secreting IL-2 and interferon-gamma under anti-CD3/anti-CD28 stimulation was significantly decreased. The level of serum soluble IL-2R (sIL-2R), a T cell activation marker, also declined. The changes in T cell reactivity were correlated with a significant increase in MHC Class II-positive mDC cells in circulation. An increase in Treg cell numbers was also observed. CONCLUSION: . The anti-TNF-alpha therapy blockaded MHC Class II-positive mDC maturation, enhanced regulatory T cell levels, and suppressed the functions of effector T cells. The reduced T cell reactivity could contribute to the efficacy of the TNF-alpha antagonist therapy in patients with 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".