Differential anti‐inflammatory and chondroprotective effects of simulated digests of indomethacin and an herbal composite (Mobility<sup>TM</sup>) in a cartilage explant model of articular inflammation
Bibliographic record
Abstract
Herbs are an increasingly popular treatment option for horses with cartilage inflammation, despite a relative paucity of research demonstrating efficacy. The research objective was to evaluate the differential anti-inflammatory and chondroprotective efficacy of a simulated digest of indomethacin and a commercially available herbal product in a cartilage model of osteoarthritis. Cartilage explant was integrated with simulated digestion of indomethacin and the herbal product in order to account, at least in part, for the actions of major digestive enzymes and pH. The resulting digests were ultrafiltrated (50 kDa), to account for absorption from the GI tract and movement into the cartilage matrix. We hypothesized that (i) a simulated digest of indomethacin would block interleukin 1 beta-(IL-1) dependent formation of prostaglandin E2 (PGE2) and nitric oxide (NO) without protecting cartilage against IL-1-induced glycosaminoglycan (GAG) release, and (ii) the herbal product would reduce PGE2 and NO in IL-1-stimulated explants, and inhibit release of GAG, in IL-1-stimulated explants. Results showed that indomethacin is an effective anti-inflammatory, evidenced by strong inhibition of IL-1-induced PGE2 and NO from cartilage explants. However, indomethacin provided no protection against IL-1-induced GAG release. Simulated digest of the herbal extract significantly inhibited IL-1-induced NO production and GAG release, while having a slight increase in PGE2. These data provide evidence for the anti-inflammatory effect of indomethacin on IL-1-stimulated cartilage explants, and the herbal product Mobility may be a useful adjunct in arthritis because of its chondroprotective properties in IL-1-stimulated cartilage.
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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".