Chaga mushroom triterpenoids as adjuncts to minimally invasive cancer therapies: A review
Bibliographic record
Abstract
Cancer has become the second leading cause of death in the world. Integrative cancer therapy management is continuously evolving to enhance treatment outcomes. Chaga mushroom (Inonotus obliquus) is a parasitic fungus acclaimed to contain pharmaceutical and nutraceutical value in the fight against cancer. In particular, triterpenoid constituents derived from Chaga mushrooms have been recognized for their anti-cancer activity after distinguished cytotoxicity was repeatedly observed in cancer cells treated in vitro with lipophilic fractions of extract compared to aqueous ones. Studies that investigate the anti-cancer activity of Chaga mushroom triterpenoids are reviewed in this article to determine which cancer cell lines demonstrate the greatest susceptibility to them while highlighting the structure-activity relationships that are involved. Triterpenoid supplementation as an adjunct to cancer treatment may be a viable option as inotodiol and 3-β-22 α-dihydroxylanosta-8, 25-diene-24-one have been shown to exhibit anti-cancer activity similar to that of conventional drugs. Advances in addressing bioavailability challenges are also included in this review as studies include in vivo components.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".