Buprenorphine Alters Inflammatory and Oxidative Stress Molecular Markers in Arthritis
Bibliographic record
Abstract
Buprenorphine is recommended for use as an analgesic in animal models including in murine models of collagen-induced arthritis (CIA). However, the effect of buprenorphine on the expression of disease-associated biomarkers is not well defined. We examined the effect of buprenorphine administration on disease progression and the expression of inflammatory and oxidative stress markers, in a murine model of CIA. Buprenorphine administration altered the expression of cytokines, IFN-γ, IL-6, and MMP-3, and oxidative markers, for example, iNOS, superoxide dismutase (SOD1), and catalase (CAT), in the CIA mice. As buprenorphine is an analgesic, we further monitored the association of expression of these biomarkers with pain scores in a human cohort of early rheumatoid arthritis (RA). Serum MMP-3 levels and blood mRNA expression of antioxidants sod1 and cat correlated with pain scores in the RA cohort. We have demonstrated that administration of buprenorphine alters the expression of inflammatory and oxidative stress-related molecular markers in a murine model of CIA. This caveat needs to be considered in animal experiments using buprenorphine as an analgesic, as it can be a confounding factor in murine studies used for prediction of response to therapy. Furthermore, the antioxidant enzymes that showed an association with pain scores in the human cohort may be explored as biomarkers for pain in future studies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".