A biological extract of turmeric (Curcuma longa) modulates response of cartilage explants to lipopolysaccharide
Bibliographic record
Abstract
Abstract Background Turmeric is commonly used as a dietary treatment for inflammation, but few studies have evaluated the direct effect of turmeric on cartilage. The purpose of this study was to characterize cartilage explants’ inflammatory responses to lipopolysaccharide in the presence of a simulated biological extract of turmeric. Methods Turmeric was incubated in simulated gastric and intestinal fluid, followed by inclusion of liver microsomes and NADPH. The resulting extract (TURsim) was used to condition cartilage explants in the presence or absence of lipopolysaccharide. Explants were cultured for 96 h (h); the first 24 h in basal tissue culture media and the remaining 72 h in basal tissue culture media containing TURsim(0, 3, 9 or 15 μg/mL). Lipopolysaccharide (0 or 5 μg/mL) was added for the final 48 H. mediasamples were collected immediately prior to lipopolysaccharide exposure (0 h) and then at 24 and 48 h after, and analyzed for prostaglandin E2(PGE2), glycosaminoglycan (GAG), and nitric oxide (NO). Explants were stained with calcein-AM for an estimate of live cells. Data were analyzed using a 2-way repeated measures (GAG, PGE2, NO) or 1-way ANOVA without repeated measures (viability). Significance accepted atp < 0.05. Results TURsimsignificantly reduced PGE2,NO and GAG, and calcein fluorescence was reduced. Conclusions: These data contribute to the growing body of evidence for the utility of turmeric as an intervention for cartilage inflammation.
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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) | 0.000 | 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".