Research Advances in the Intervention of Inflammation and Cancer by Active Ingredients of Traditional Chinese Medicine
Bibliographic record
Abstract
A large body of evidence has shown that inflammation and cancer are strongly related. Thus anti-inflammatory agents have been investigated for cancer prevention and treatment in preclinical and clinical studies, including the nonsteroidal anti-inflammatory drugs (NSAIDs) and traditional Chinese medicine (TCM). In TCM, there exist a wide range of biologically active substances, such as saponins, flavonoids, alkaloids, polysaccharides, polyphenols, phenylpropanoids, and quinones. Many of these active ingredients have been reported to inhibit inflammation, activate inflammatory immune response, and/or inhibit cancer cell proliferation and tumor growth. Given the potential role of inflammation in cancer initiation and progression, the inflammatory tumor microenvironment, the cross-talks between inflammatory and cancer cells, and multitargeting activities of some TCM compounds, we summarize the current knowledge on the anti-inflammatory and anti-cancer properties of ingredients of TCM together with their underlying mechanisms in an integrated way. We hope to provide a reliable basis and useful information for the development of new treatment strategies of inflammation and cancer comprehensively using TCM and their active ingredients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".