Revealing Novel Source of Breast Cancer Inhibitors from Seagrass Enhalus acoroides: In Silico and In Vitro Studies
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".