Elevated Bioactivity of Ruta graveolens against Cancer Cells and Microbes Using Seaweeds
Bibliographic record
Abstract
Human cancer and pathogenic microbes cause a significant number of deaths every year. Modulating current sources of natural products that control such diseases becomes essential. Natural algae, such as Ascophyllum nodosum and Ecklonia maxima, can modulate the metabolic processes as well the bioactivities of Ruta graveolens L. The R. graveolens plants were subjected to nine soil drenches of A. nodosum (7 mL L−1), E. maxima (7 mL L−1), or both extracts. Morphological performance, gas exchange parameters, and essential oils (EOs) composition (GC-MS) were studied and the bioactivity was assessed against several cancer cells and pathogenic bacteria or fungi. Treatment with A. nodosum + E. maxima seaweed extracts (SWE) led to the highest morphological performance and gas exchange parameters. The highest antiproliferative, apoptotic, and caspase-3/7 activities of EO were against HeLa in SWE mixture treated plants. The best EO antimicrobial activities were obtained against Staphylococcus aureus and Penicillium ochrochloron. SWE mixtures treated plants showed the best bioactivities against microbes and cancer cells. The highest abundance of 2-undecanone (62%) and 2-nonanone (18%) was found in plants treated with SWE mixtures and caused the best anticancer and antimicrobial effects. Seaweed mixtures act as natural elicitors of pharmaceutical industries and favored 2-undecanone and 2-nonanone in R. graveolens.
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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".