Chemical composition, antioxidant, antimicrobial and antiproliferative activity of Laureliopsis philippiana essential oil of Chile, study in vitro and in silico
Bibliographic record
Abstract
Chilean Laureliopsis philippiana has been used in traditional medicine by the Mapuche and their ancestors. To evaluate its pharmacological activity, Laureliopsis philippiana leaf essential oil extract (LP_EO) was chemically and biologically characterized in the present study. In vitro antioxidant potential was analyzed, and antitumor activity was evaluated in non-tumor and tumor cell culture lines. Caenorhabditis elegans was used as a model for evaluating toxicity, and the chemical composition of the essential oil was analyzed using gas chromatography–mass spectrometry. The oil contains six major monoterpenes: eucalyptol (27.7 %), linalool (27.6 %), isozaphrol (19.5 %), isohomogenol (12.6 %), α-terpineol (7.7 %), and eudesmol (4.8 %). Based on quantum mechanical calculations, isosafrole and isohomogenol conferred in vitro antioxidant and antimicrobial activity to LP_EO. In addition, LP_EO showed antimicrobial activity against clinical Helicobacter pylori isolates (MIC 64 and MBC > 128 μg·mL−1), Staphylococcus aureus (MIC 32 and MBC > 64 μg·mL−1), Escherichia coli (MIC 8 and MBC 16 μg·mL−1) and Candida albicans (MIC 64 and > 128 μg·mL−1). LP_EO could selectively inhibit the proliferation of epithelial tumor cell lines but showed low toxicity against Caenorhabditis elegans (0.39 to 1.56 μg·mL−1). Therefore, LP_EO may be used as a source of bioactive compounds in novel pharmacological treatments for veterinary and human application, cosmetics, or sanitation.
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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 | 0.001 | 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".