Anti-proliferating and antioxidant properties of <i>Eriobotrya japonica</i> fruit in human breast cancer cells combined with <i>in silico</i> study
Bibliographic record
Abstract
This study aimed to identify compounds in the fruits of the medicinal Eriobotrya japonica L, determine anti-apoptotic properties in human breast cancer cells and predict the mechanism via docking and bioinformatic analyses. HPLC-ESI-HRMS/MS metabolite profiling of E. japonica ethanolic extract allowed the identification of eight compounds. Subsequent network pharmacology and molecular docking simulations targeting key cancer-related proteins showed excellent predictive activity (Pa > 0.71) for seven of the compounds. Two of the compounds hyperoxide (C6) and afzelin (C8) showed strong affinities with inducible nitric oxide synthase (iNOS) and beta-1 adrenergic receptor-β (ADRB1) with ΔG of −11.5 and −12.1, respectively. Radical scavenging data showed compound C8 (EC50 89.1 µg/mL, ABTS+•; 96.1 µg/mL, DPPH) was the most active in relation to other compounds and the extract (EC50 115.5 µg/mL, ABTS+•; 101.5 µg/mL, DPPH). The cytotoxicity (LD50) was 96.1–178.9 µg/mL) for cancerous MCF-7 cells compared to 1,105-2,066 µg/mL for normal MCF-10A cells.
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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".