Effects of Hexane Root Extract of<i>Ferula hermonis</i>Boiss. on Human Breast and Colon Cancer Cells: An<i>In Vitro</i>and<i>In Vivo</i>Study
Bibliographic record
Abstract
Breast and colon cancers are leading causes of cancer-related deaths globally. Plants are a potential source of natural products that may be used for the treatment of cancer.Ferula hermonis(FH) is reported to have diverse therapeutic effects. However, there are few reports on thein vitroanticancer potential of FH extract. Our results showed that theFerula hermonisroot hexane extract (FHRH) can induce dose-dependent cytotoxic effects in breast and colon cancer cells with MTT IC50values of 18.2 and 25μg/ml, respectively. The FHRH extract induced apoptosis in both breast and colon cancer cells; this was confirmed by light and nuclear staining, q-PCR, and caspase 3/7 activation. This study also demonstrated the antitumor activity of FHRH in 9,10-dimethylbenz[α]anthracene DMBA-induced rodent mammary tumor model. The GC/MS analysis revealed the presence of 3,5-Dimethylbenzenemethanol, Alpha-Bisabolol, Alpha-pinene, Beta-pinene, and Baccatin III that have various pharmacological potentials. Overall, the present study suggests that FHRH extract possesses anticancer potential which is mediated through apoptotic effects in MDA-MB-231 and LoVo cells. The present study also considered a basis for further investigations into the potential use of FHRH extract as an anticancer therapy for breast and colon cancers.
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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".