Anti-inflammatory effects of the combined extracts of Achyranthes japonica nakai and Aralia continentalis kitagawa in vitro and in vivo
Bibliographic record
Abstract
This study investigated the anti-inflammatory effects of mixed extracts of Achyranthes japonica Nakai (AJ) and Aralia continentalis Kitagawa (AC) (ratios of 1:2, 1:3, 1:5, 2:1, 3:1 and 5:1) on RAW264.7 macrophages and evaluated the anti-inflammatory effects of the mixed extracts of AJ and AC by measuring IL-1β, IL-6, and TNFα using the ELISA kit assay. In particular, the formation of nitric oxide (NO) was found to decrease in the group treated with the combined extracts of AJ and AC at all ratios. In particular, extracts of ratio of 2:1 (AJ:AC) deceased the formation of NO level that is approximately 60% of the group treated with only lipopolysaccharide (LPS). Also, extracts of ratio of 2:1 (AJ:AC) reduced the production of IL-1β, IL-6, TNFα and PGE2 with statistical significance. Volunteers over the age of 50 who complain of discomfort in knee joints were selected as the experimental subjects. The subjects took daily administration of 2000 mg of the combined extracts of ratio of 2:1 (AJ:AC) for 12 weeks. A survey (VAS (Visual Analog Scale), WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index)) was conducted after the 12 weeks of oral administration. The experimental group showed the change between each visit and baseline time compared with the control group. In the intention-to-treat (ITT) analysis, VAS score and WOMAC stiffness score decreased significantly. And the WOMAC total score and function score tended to decrease. In the per-protocol (PP) analysis, the WOMAC stiffness score was significantly decreased and the VAS and WOMAC total and function scores were decreased. There was no significant difference in all parameters of ITT and PP in radiological examinations.
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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) | 0.001 | 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 | 0.000 |
| 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".