Chaga ( <i>Inonotus obliquus</i> ), a Future Potential Medicinal Fungus in Oncology? A Chemical Study and a Comparison of the Cytotoxicity Against Human Lung Adenocarcinoma Cells (A549) and Human Bronchial Epithelial Cells (BEAS-2B)
Bibliographic record
Abstract
BACKGROUND: Inonotus obliquus, also known as Chaga, is a parasitic fungus growing on birches and used in traditional medicine (especially by Khanty people) to treat various health problems. In this study, we aimed to quantify the 3 metabolites frequently cited in literature, that is, betulin, betulinic acid, and inotodiol in the Chaga recently discovered in forests located in Normandy (France), and to compare their concentrations with Ukrainian and Canadian Chaga. This study also explores the cytotoxicity of the French Chaga against cancer-derived cells and transformed cells. METHODS: A quantification method by HPLC-MS-MS (high-performance liquid chromatography-tandem mass spectrometry) of betulin, betulinic acid, and inotodiol was developed to study the French Chaga and compare the concentration of these metabolites with extracts provided from Chaga growing in Canada and Ukraine. This method was also used to identify and quantify those 3 compounds in other traditional preparations of Chaga (aqueous extract, infusion, and decoction). Among these preparations, the aqueous extract that contains betulin, betulinic acid, and inotodiol was chosen to evaluate and compare its cytotoxic activity toward human lung adenocarcinoma cells (A549 line) and human bronchial epithelial cells (BEAS-2B line). RESULTS: French Chaga contains betulin and betulinic acid at higher levels than in other Chaga, whereas the concentration of inotodiol is greater in the Canadian Chaga. Moreover, the results highlighted a cytotoxic activity of the Chaga's aqueous extract after 48 and 72 hours of exposure with a higher effect on cancer-derived cells A549 than on normal transformed cells BEAS-2B ( P = 0.025 after 48 hours of exposure and P = 0.004 after 72 hours of exposure).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".