Revealing Novel Source of Breast Cancer Inhibitors from Seagrass Enhalus acoroides: In Silico and In Vitro Studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Enhalus arcoides is a highly beneficial type of seagrass. Prior studies have presented proof of the bioactivity of E. acoroides, suggesting its potential to combat cancer. Therefore, this study aims to delve deeper into E. acoroides bioactive molecule profiles and their direct biological anticancer activities potentials through the combination of in-silico and in-vitro studies. This study conducted metabolite profile analysis on E. acoroides utilizing HPLC-ESI-HRMS/MS analysis. Two extraction techniques, ethanol and hexane, were employed for the extraction process. Furthermore, the in-silico study was conducted using molecular docking simulations on the HER2, EGFR tyrosine kinase and HIF-1α protein receptor. Afterward, the antioxidant activity of E. acoroides metabolites was examined to ABTS, and the antiproliferative activity was tested using an MTT assay. An in-silico study revealed its ability to combat breast cancer by inhibiting the HER2/EGFR/HIF-1α pathway through molecular docking. In addition, the MTT assay demonstrated that higher dosages of metabolites from E. acoroides increased the effectiveness of toxicity against cancer cell lines. Additionally, the study demonstrated that the metabolites possess the ability to function as potent antioxidants, effectively inhibiting a series of carcinogenic mechanisms. Ultimately, this study showed a new approach to unveiling the E. acoroides metabolites’ anticancer activity through inhibiting HER2/EGFR/HIF-1α receptors, with great cytotoxicity and a potent antioxidant property to prevent a carcinogenic cascade.
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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) | 0.000 | 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) | 0.000 | 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 it