The Tumor Necrosis Factor-α-blocking Agent Infliximab Inhibits Interleukin 1ß (IL-1ß) and IL-6 Gene Expression in Human Osteoblastic Cells
Bibliographic record
Abstract
OBJECTIVE: Tumor necrosis factor-alpha (TNF-alpha) is a proinflammatory cytokine involved in the pathogenesis of several rheumatic diseases, including rheumatoid arthritis (RA), associated with systemic bone loss and subchondral bone erosions. TNF-alpha-blocking agents such as infliximab have been successful in treatment of disease-modifying antirheumatic drug-resistant rheumatic diseases. Infliximab therapy in RA also had beneficial effects on local bone destruction and bone mineral density. We assessed effects of infliximab treatment on the bone tissue compartment and cytokine profile expression in vitro. METHODS: Osteoblast-like cells were exposed for 24 h to sera of RA patients collected at baseline and after 1 month (T1) and 3 years (T2) of infliximab treatment. Total RNA was extracted, and expression of interleukin 1beta (IL-1beta), IL-6, and osteoprotegerin (OPG) was measured by RT-PCR. RESULTS: IL-1beta gene expression was significantly reduced by the T1 serum, and the same decrease was elicited by the T2 serum. IL-6 downregulation was evident with the T2 serum. OPG was unaffected. CONCLUSION: The finding of downregulation of inflammatory cytokines was interesting, particularly IL-6, which plays a crucial role in arthritis-related bone loss due to its involvement in osteoclast recruitment and activation. These results may represent a biological explanation and a link for the clinical observation of the beneficial effects of anti-TNF-alpha agents on the progression of rheumatic diseases at the bone level.
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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 | 0.001 |
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".