Evaluation of the novel avocado/soybean unsaponifiable Arthrocen to alter joint pain and inflammation in a rat model of osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Avocado/soybean unsaponifiables such as Arthrocen have been reported to reduce cartilage catabolism and chondrocytic synthesis of inflammatory mediators associated with osteoarthritis (OA). While there is some clinical evidence that avocado/soybean unsaponifiables can reduce OA pain, no preclinical studies have corroborated this observation. The present study determined whether addition of an avocado/soybean unsaponifiable (Arthrocen) to the drinking water of OA rats reduced direct and referred joint pain. METHODS: OA was induced in male Wistar rats by intra-articular injection of sodium monoiodoacetate (MIA: 0.3mg) and animals were allowed to recover for 14 days. Arthrocen was added to the drinking water which was available to animals ad libitum. On day 30, joint pain was assessed by dynamic incapacitance while referred pain was determined by von Frey hair algesiometry. RESULTS: The joint damage induced by MIA injection was severe and was consistent with end-stage OA. Arthrocen consumption (approximately 35 mg/day) attenuated the joint oedema associated with MIA injection. Hindlimb weight bearing also significantly improved in Arthrocen-treated rats (P<0.05); however, von Frey hair mechanosensitivity was unaffected by this treatment. CONCLUSIONS: These data indicate that Arthrocen has the potential to reduce joint inflammation and pain associated with end-stage OA.
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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) | 0.001 | 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".