Inhibitory effect of ginsenoside-Rd on carrageenan-induced inflammation in rats
Bibliographic record
Abstract
A previous study reported that ginsenoside-Rd reduced the production of tumor necrosis factor-α by inhibiting nuclear factor-κB in lipopolysaccharide-activated N9 microglia in vitro. The aim of the present study was to confirm the anti-inflammatory effects and mechanisms of ginsenoside-Rd in animal experiments involving acute inflammation. The results indicated that ginsenoside-Rd at doses ranging from 12.5 to 50 mg/kg i.m. significantly inhibited the swelling of hind paws in rats for 1-6 h after the carrageenan injection. The levels of proinflammatory cytokines and proinflammatory mediators were markedly reduced by ginsenoside-Rd. Ginsenoside-Rd, when administered intramuscularly at 12.5, 25, and 50 mg/kg doses, showed signicant inhibition of carrageenan-induced production of interleukin-1β (6.91%, 45.75%, and 55.18%, respectively), tumor necrosis factor-α (37.99%, 56.39%, and 47.38%, respectively), prostaglandin E(2) (22.92%, 30.12%, and 36.36%, respectively), and nitric oxide (28.27%, 44.53%, and 53.42%, respectively). In addition, ginsenoside-Rd (12.5, 25, and 50 mg/kg i.m.) effectively decreased the levels of nuclear factor-κB (6.77%, 20.28%, and 41.03%, respectively) and phosphorylation of IκBα (13.23%, 26.92%, and 41.80%, respectively) in the carrageenan-inflamed paw tissues. These results suggest that ginsenoside-Rd has significant anti-inflammatory effects in vivo, which might be due to its blocking of the nuclear factor-κB signaling pathway. Thus, it may be possible to develop ginsenoside-Rd as a useful agent for inflammatory diseases.
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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 | 0.001 | 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) | 0.001 | 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".