Interleukin 17 (IL-17) Increases the Expression of Toll-like Receptor-2, 4, and 9 by Increasing IL-1β and IL-6 Production in Autoimmune Arthritis
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of interleukin 17 (IL-17) on the expression of Toll-like receptor (TLR)-2, 4, and 9 in collagen-induced arthritis (CIA) in mice. METHODS: On Days 28 and 32 after induction of CIA in mice, phosphate-buffered saline (PBS group) or IL-17 (IL-17 group) was injected into both knee joints. On Day 35, mice were sacrificed. The severity of knee joint arthritis, synovial inflammation, and bone destruction was measured by a scoring system using macrography and histological analysis. Synovial expression of TLR-2, 4, 9, IL-17, IL-1beta, tumor necrosis factor-alpha (TNF-alpha), and IL-6 was determined by real-time PCR and immunohistochemistry. Synoviocytes of CIA mice were cultured with IL-17 and with neutralizing antibodies to cytokine, and the expression of TLR-2, 4, 9, IL-1beta, TNF-alpha, and IL-6 was determined by real-time RT-PCR. RESULTS: In CIA mice, knee arthritis scores, synovial inflammation, bone destruction scores, and expression of synovial TLR-2, 4, and 9, IL-17, IL-1beta, TNF-alpha and IL-6 were higher in the IL-17 and PBS groups than in normal DBA1 mice. These variables were also significantly higher in the IL-17 group than in the PBS group. In CIA synoviocytes, IL-17 increased the expression of TLR-2, 4, and 9, and this effect was significantly alleviated by neutralizing antibodies to IL-17, IL-1beta, and IL-6. CONCLUSION: IL-17 aggravates joint inflammation and destruction, and increases the synovial expression of TLR-2, 4, and 9 by increasing IL-1beta and IL-6. These results imply that the IL-17-induced increase in expression of TLR-2, 4, and 9, and IL-1beta and IL-6 production are involved in the IL-17-induced aggravation of arthritis.
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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) | 0.001 | 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 | 0.001 |
| 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".