Syagrus coronata seed oils have antimicrobial action against multidrug-resistant Staphylococcus aureus
Bibliographic record
Abstract
Syagrus coronata (Mart.) Becc. (Arecaceae) is a native Brazilian palm (ouricuri) and despite the use of its derived products by traditional communities, few scientific reports have been published regarding its biomedical activity. This study investigates the chemical composition and anti-Staphylococcus aureus effects of both manufactured oil (SCO) and essential oil (SCEO) from S. coronata seeds. SCO was provided by rural inhabitants, while SCEO was obtained by hydrodistillation. Chemical characterization was performed by gas chromatography-mass spectrometry (GC/MS). In vitro antimicrobial activity was determined against 17 S. aureus strains, including multidrug-resistant strains. Eleven compounds were detected in the SCEO, octanoic (28.61%) and dodecanoic acids (22.97%) were the major constituents. On the other hand, nineteen fatty acids (FA) were identified in the SCO, the major ones were dodecanoic acid (41.58%) and 9-octadecenoic acid (23.81%). Both oils showed strong activity against all tested strains. Most strains (68.75%) were sensitive to SCEO at minimum inhibitory concentrations (MIC) between 0.002 and 0.01 μL/mL; and minimum bactericidal concentrations (MBC) ranging from 0.002 to 0.312 μL/mL. SCO inhibited the growth of 52.94% of strains with MIC between 0.16 and 0.625 μL/mL. MBC values for SCO were between 0.16 and 5 μL/mL; however, 47.05% of isolates were killed by 2.5 μL/mL of SCO. These results encourage further research into the toxicological and pharmacological aspects of SCO and SCEO. Such work would likely support their use in the development of new antimicrobial agents for the pharmaceutical, food and cosmetic industries. Key words: Caatinga, essential oil, anti-Staphylococcus aureus, natural products.
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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".