Evaluation of the Anti-inflammatory Potential of Chinese Botanicals
Bibliographic record
Abstract
Traditional Chinese medicine (TCM) has been using about 1300 plants for anti-inflammatory purposes. Activity guided isolation in a medium throughput approach has resulted in the discovery of a number of new drug leads. From the fruits of Evodia rutaecarpa (Juss.) Benth. (Rutaceae), several quinolinone alkaloids, like 1-methyl-2-nonyl-4(1H)-quinolinone, 1-methyl-2-(6Z)-6-undecenyl-4(1H)-quinolinone, 1-methyl-2-(4Z,7Z)-4,7-tridecadienyl-4(1H)-quinolinone, evo-carpine and 1-methyl-2-(6Z,9Z)-6,9-pentadecadienyl-4(1H)-quinolinone, have been isolated which showed strong inhibitory activity on leukotriene biosynthesis in human polymorphonuclear granulocytes [1]. They showed no cytotoxic activity and might bind to the lipid binding site of 5-LOX [2]. Moreover they were very effective against mycobacteria [3]. From Centipeda minima (L.) A. Braun & Asch. (Asteraceae) a series of sesquiterpene lactones, like 6-O-methylacrylplenolin, 6-O-angeloylplenolin and 6-O-tigloyl-plenolin, have been isolated, which exhibited strong inhibitory properties on inducible nitric oxide synthase (iNOS) in RAW 264.7 macrophages [4]. Extracts from Chinese herbs have shown inhibitory properties on expression of NF-kB1 in THP-1 cells [5]. Overexpression of this transcription factor is associated with inflammatory diseases, like rheumatoid arthritis, atherosclerosis, asthma and inflammatory bowel disease. Sesquiterpenes, like parthenolide and helenalin have been demonstrated to be responsible for that effect [6]. Acknowledgements: Part of the work has been supported by Zukunftsfonds Styria (“TCM Research Center Graz”). References: [1] Adams M, et al. (2004) Planta Med. 70: 904–908. [2] Adams M, et al. (2007) Planta Med. 73: 1554–1557. [3] Adams M, et al. (2005) Int. J. Antimicrob. Agents 26(3): 262–264. [4] Fischer M, et al. (2009) in preparation. [5] Gusenleitner S, Bauer R, (2007) Planta Med. 73: 844. [6] Gusenleitner S, et al. (2009) in preparation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".