A map describing the association between effective components of traditional Chinese medicine and signaling pathways in cancer cells in vitro and in vivo
Bibliographic record
Abstract
Cancer is the second leading cause of death by disease in the world. Chemotherapy is one of three major therapeutic methods for cancer treatment, but cancer cells gradually evolve resistance to chemotherapeutic reagents. For centuries, traditional Chinese medicine (TCM) was used to fight against cancer. In recent years, a number of effective component mechanisms of TCM have been increasingly illuminated. As we know, chemical structures of reagents decide or affect their activities on target pathways. Thus, we classified some antitumor-related TCM components reported in the last five years into thirteen groups by their chemical structures, such as, alkaloids, diterpenoids, triterpenes, sesquiterpenes, anthraquinones, benzoquinones, flavonoids, berbamines, xanthones, saponins, steroids, polysaccharides, and glycosides. In various cancer cell lines, these constituents target dozens of signaling pathways in vitro and in vivo. Among these components, there are three sets: i) mainly apoptosis-related groups, such as, alkaloids, diterpenoids, anthraquinones, berbamines, and xanthones, target pathways like the mitochondrial pathway, NF-κB pathway, p53 pathway and so on; ii) mainly proliferation, invasion and metastasis-related groups, such as, triterpenes, sesquiterpenes, polysaccharides, and glycosides, target pathways like the mTOR pathway, β-catenin pathway, ERK pathway and so on; iii) both apoptosis and proliferation, invasion and metastasis-related groups, such as benzoquinones, flavonoids, saponins, and steroids, target the pathways in i) and ii) synchronously. These will provide association information between TCM components and signaling pathways to promote studies on mechanisms of effective constituents, target drug development, and combinational chemotherapy. TCM could be alternative medicine for cancer treatment in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".