Phytochemistry, Antioxidant Activity and Cytotoxicity Evaluation of Black-White Fungus Auricularia sp. against Breast MCF-7 Cancer Cells
Bibliographic record
Abstract
Cancer is a non-communicable disease with over 18.1 million new cases and 9.6 million deaths annually according to WHO. Breast cancer is the number two highest count type of cancer trailing behind lung cancer. Treating cancer is expensive and have various side effects. Active components found in plants or fungi that have antioxidant and cytotoxic activity towards cancer cells, could be an alternative for anticancer. One of the fungi that is potentially developed as an anticancer, are the genus of Auricularia sp. also known as black-white fungus. This study aims to determine the phytochemicals components, antioxidant activity and cytotoxic effect of the Auricularia sp. towards MCF-7 breast cancer cells. Methods: Dried black-white fungus of Auricularia sp. grinded into a fine powder. Then, multilevel maceration is done with the n-hexane, ethyl acetate, ethanol as solvents. The extracts of black-white fungus undergo phytochemical screening and thin layer chromatography (TLC), followed by measuring antioxidant and evaluating the cytotoxic activity towards MCF-7 breast cancer cells. Results: black-white fungus of Auricularia sp. contained secondary metabolites of flavonoids, alkaloids, and triterpenoids and a total of 17 other phytochemical components. Ethyl acetate extract of black-white fungus showed a weak antioxidant activity towards DPPH free radical with IC 50 of 215.51 g/mL and a very active cytotoxic activity on MCF-7 cells with IC 50 of 0.21 g/mL. On the other hand, ethanol and n-hexane extracts of black-white fungus are categorized with an active cytotoxic activity on MCF-7 cells with IC 50 of 29.28 g/mL and 50.39 g/mL, respectively. Conclusion: Black-white fungus Auricularia sp. that had anticancer activity towards breast MCF-7 cells should be considered as an alternative treatment for breast cancer therapy.
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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 | 0.001 | 0.001 |
| 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".