Synergistic Cytotoxicity of Extracts of Chaga Mushroom and Microalgae against Mammalian Cancer Cells In Vitro
Bibliographic record
Abstract
Chaga mushroom (Inonotus obliquus) contains bioactive metabolites and has been used to treat various ailments, including cancer. Similarly, marine microalgae are considered a sustainable food supplement with anticancer and antioxidant properties. This study investigated the cytotoxicity of different extracts prepared from I. obliquus and microalgae using cultured human and canine cancer cell lines (MCF-7, HepG2, HOS, D-17, and DH-82). MTS cell viability assay was used to study the cytotoxicity of I. obliquus and microalgae extracts, and a synergy matrix effect was used to study the combined effect of the extracts. Isobologram analysis and the highest single agent synergy model were applied to study and validate the synergy between the extracts from I. obliquus and microalgae. Ethanol-based extraction and supercritical water extract significantly inhibited the growth of various mammalian cancer cells compared to aqueous extracts. Osteosarcoma cells were more susceptible to the supercritical extracts of I. obliquus and chlorophyll-free and sugar-free ethanol extracts of microalgae. A combination of ethanol-based I. obliquus extract and chlorophyll-free microalgae extract resulted in a synergistic interaction with various tested cancer cells. This study provides experimental evidence supporting the potential therapeutic application of I. obliquus and microalgae extracts with a synergistic effect to inhibit the growth of various mammalian cancer cells. Additional in vivo studies are required to fully explore possible therapeutic applications of these unique mixtures to be used in treating cancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".