The Daiokanzoto (TJ-84) Kampo Formulation Reduces Virulence Factor Gene Expression in Porphyromonas gingivalis and Possesses Anti-Inflammatory and Anti-Protease Activities
Bibliographic record
Abstract
Kampo formulations used in Japan to treat a wide variety of diseases and to promote health are composed of mixtures of crude extracts from the roots, bark, leaves, and rhizomes of a number of herbs. The present study was aimed at identifying the beneficial biological properties of Daiokanzoto (TJ-84), a Kampo formulation composed of crude extracts of Rhubarb rhizomes and Glycyrrhiza roots, with a view to using it as a potential treatment for periodontal disease. Daiokanzoto dose-dependently inhibited the expression of major Porphyromonas gingivalis virulence factors involved in host colonization and tissue destruction. More specifically, Daiokanzoto reduced the expression of the fimA, hagA, rgpA, and rgpB genes, as determined by quantitative real-time PCR. The U937-3xκB-LUC monocyte cell line transfected with a luciferase reporter gene was used to evaluate the anti-inflammatory properties of Daiokanzoto. Daiokanzoto attenuated the P. gingivalis-mediated activation of the NF-κB signaling pathway. It also reduced the secretion of pro-inflammatory cytokines (IL-6 and CXCL8) by lipopolysaccharide-stimulated oral epithelial cells and gingival fibroblasts. Lastly, Daiokanzoto, dose-dependently inhibited the catalytic activity of matrix metalloproteinases (-1 and -9). In conclusion, the present study provided evidence that Daiokanzoto shows potential for treating and/or preventing periodontal disease. The ability of this Kampo formulation to act on both bacterial pathogens and the host inflammatory response, the two etiological components of periodontal disease, is of high therapeutic interest.
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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".