Interleukin‐17 synergizes with IFNγ or TNFα to promote inflammatory mediator release and intercellular adhesion molecule‐1 (ICAM‐1) expression in human intervertebral disc cells
Bibliographic record
Abstract
Interleukin-17 (IL-17) is a cytokine recently shown to be elevated, along with interferon-γ (IFNγ) and tumor necrosis factor (TNFα), in degenerated and herniated intervertebral disc (IVD) tissues, suggesting a role for these cytokines in intervertebral disc disease. The objective of our study was to investigate the involvement of IL-17 and costimulants IFNγ and TNFα in intervertebral disc pathology. Cells were isolated from anulus fibrosus and nucleus pulposus tissues of patients undergoing surgery for intervertebral disc degeneration or scoliosis. The production of inflammatory mediators, nitric oxide (NOx), prostaglandin E2 (PGE2) and interleukin-6 (IL-6), as well as intercellular adhesion molecule (ICAM-1) expression, were quantified for cultured cells following exposure to IL-17, IFNγ, and TNFα. Intervertebral disc cells exposed to IL-17, IFNγ, or TNFα showed a remarkable increase in inflammatory mediator release and ICAM-1 expression (GLM and ANOVA, p < 0.05). Addition of IFNγ or TNFα to IL-17 demonstrated a synergistic increase in inflammatory mediator release, and a marked increase in ICAM-1 expression. These findings suggest that IVD cells not only respond with a catabolic phenotype to IL-17 and costimulants IFNγ and TNFα, but also express surface ligands with consequent potential to recruit additional lymphocytes and immune cells to the IVD microenvironment. IL-17 may be an important regulator of inflammation in the IVD pathologies.
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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) | 0.002 | 0.001 |
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".