Anti‐inflammatory and chondroprotective effects of nutraceuticals from Sasha's Blend in a cartilage explant model of inflammation
Bibliographic record
Abstract
New Zealand green lipped mussel (NZGLM), abalone (AB), and shark cartilage (SC) are extensively used for treatment of and/or as preventatives for arthritis, despite a relative paucity of scientific evidence for efficacy. This research integrated a simulated digestion protocol with ultrafiltration and cartilage explants to generate new information on the anti-inflammatory and chondroprotective properties of NZGLM, SC, and AB. Each nutraceutical was artificially digested using simulated gastric and intestinal fluids, and the crude digest was ultrafiltered (50 kDa). Each filtrate was applied individually to cartilage explants before the explants were stimulated with IL-1 to induce an acute inflammatory response. Media were collected daily for 48 h and analyzed for prostaglandin E(2) (PGE(2)), glycosaminoglycan (GAG), and nitric oxide (NO), and cartilage tissue was differentially stained to determine the relative proportion of live and dead cells. SC and NZGLM significantly inhibited IL-1-induced PGE(2) synthesis and IL-1-induced GAG release, and AB was an effective inhibitor of IL-1-induced NO production. The three test nutraceuticals affect at least three major pathways involved in the catabolic cycle of arthritis and may prove important treatments and/or preventatives for the pain and degradation associated with this condition. The methodology and results describe a useful model for evaluating dietary nutraceuticals in vitro.
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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".