Systemic Sclerosis Fibroblasts Show Specific Alterations of Interferon-γ and Tumor Necrosis Factor-α-induced Modulation of Interleukin 6 and Chemokine Ligand 2
Bibliographic record
Abstract
OBJECTIVE: We evaluated the effect of interferon-γ (IFN-γ) and/or tumor necrosis factor-α (TNF-α) on the secretion of prototype proinflammatory cytokine interleukin 6 (IL-6), compared to T-helper 1 [Th1; chemokine (C-X-C motif) ligand 10 (CXCL10)] or Th2 [chemokine (C-C motif) ligand 2 (CCL2)] chemokines, in primary cultured fibroblasts from patients with systemic sclerosis (SSc) at an early stage of the disease. METHODS: Fibroblast cultures from 5 SSc patients (disease duration < 2 yrs) and 5 healthy controls were evaluated for the production of IL-6, CXCL10, and CCL2 at the basal level and after stimulation with IFN-γ and/or TNF-α. RESULTS: SSc fibroblasts basally produced higher levels of IL-6 than controls, while no difference was observed about CCL2 and CXCL10. TNF-α was able to dose-dependently induce IL-6 and CCL2 secretion in SSc, but not in control fibroblasts. By stimulation with increasing doses of IFN-γ, SSc fibroblasts were induced to secrete CCL2 and CXCL10, while no effect was observed on IL-6. The combination of IFN-γ and TNF-α induced a strong secretion of IL-6 and CCL2 in SSc fibroblasts but not in controls. In contrast, the synergistic effect of IFN-γ and TNF-α on CXCL10 secretion was similar in SSc fibroblasts and in controls. CONCLUSION: SSc fibroblasts participate in the self-perpetuation of inflammation by releasing IL-6, CXCL10, and CCL2 under the influence of IFN-γ and/or TNF-α. SSc fibroblasts are more active than controls in the secretion of IL-6 at baseline, and in the production of IL-6 and CCL2 under the combined IFN-γ/TNF-α stimulation.
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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".