Identification of novel functional compounds from forest onion and its biological activities against breast cancer
Bibliographic record
Abstract
The discovery of new molecules from natural sources for the treatment of tumors such as breast cancers is of importance for the development of functional foods and to the pharmaceutical industry. A natural resource with potential activity against breast cancer is forest onion, Eleutherine bulbosa (Mill.) Urb., but the identity of its active constituents and their mechanisms of action remain unexplored. Therefore, this study focuses on metabolite profiling, in silico or pharmacoinformatic activity and mechanisms, as well as advanced validation on in vitro cell lines. Ten compounds identified in E. bulbosa bulb ethanolic extract (EBE) showed cancer receptor and radical inhibitory activity via network pharmacology and molecular docking simulation. The most promising compound was avenasterol binding PARP-1, HER2, iNOS receptors with values of −11.26, −8.34, and −9.17 μg/mL, respectively. EBE and avenasterol had a smaller EC50 value, or higher potency, than the control antioxidant Trolox in radical scavenging tests with ABTS and DPPH. In line with the in silico study, EBE and avenasterol showed antiproliferative activity against human breast cancer MCF-7 with LD50 217.8 μg/mL, with relatively low cytotoxicity to normal MCF-10A cells (LD50 > 1000 μg/mL). The antiproliferative mechanism of EBE on MCF-7 was associated with downregulation of TGF-β, HER2, PI3K, and AKT which are known tumor activators. Significant (p < 0.05) upregulation of tumor suppressor gene miR-29a-3p in MCF-7 was observed after treatment with EBE in a dose-dependent manner.
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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 | 0.001 |
| 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".