Medical Wonders of <i>Chrysanthemum morifolium</i>: Fusion of Traditional Chinese Medicine and Modern Science
Bibliographic record
Abstract
Chrysanthemum is an important component of traditional Chinese medicine (TCM), has been highly praised for its special therapeutic effects for over 2000 years.This study integrate the research results of traditional and modern medicinal effects of chrysanthemum, in order to broaden its application prospects and develop new drugs, health products, etc.The active ingredients of chrysanthemum, such as naringenin and apigenin, have shown antidepressant effects by regulating various metabolic pathways such as tryptophan and phenylalanine metabolism; The synergistic effect of active ingredients such as apigenin and kaempferol in chrysanthemum has the effect of treating diseases such as gout by inhibiting the activity of xanthine oxidase.Chrysanthemum extract exhibits strong antibacterial properties against Streptococcus mutans and other bacteria, and is expected to become a candidate drug for the treatment of postoperative sore throat.Ethnic pharmacology research emphasizes that chrysanthemum extract has multiple pharmacological activities, including anti-inflammatory, antioxidant, and therapeutic effects on cardiovascular diseases.Combining traditional uses with modern medical research achievements, exploring the potential of chrysanthemum as a modern medical raw material.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".