GC/MS profiling of the essential oils from <i>Ferula xylorhachis</i> Rech.f. roots and aerial parts and assaying their antimicrobial activity against human skin pathogens
Bibliographic record
Abstract
The plants of the genus Ferula (Apiaceae), with more than 180 species found worldwide, have been a source of numerous bioactive secondary metabolites. In this study, we isolate the essential oils from different parts of an Iranian endemic species Ferula xylorhachis Rech.f. -for the first time- by hydro-distillation and further characterized their chemical profile by GC/MS analysis. In addition, we evaluated their antimicrobial properties against a set of skin pathogens, namely Staphylococcus aureus, Pseudomonas aeruginosa and Candida albicans. GC/MS analysis of the essential oils resulted in the identification of seventy-two compounds representing 98% and 90.2% of all volatile compounds in the aerial parts and the roots, respectively. Some of these compounds, mainly the monoterpenes and aliphatic hydrocarbons, were also detected in headspace GC/MS analysis of the dried plant parts. The oil compositions were dominated by the monoterpene hydrocarbons in the aerial parts, and aliphatic hydrocarbons in the roots. The principal chemical constituents were sabinene (17.6%), α-pinene (16.2%) and n-nonane (14%) in the essential oil from the aerial parts, and n-nonane (56.5%), 10s,11s-himachala-3(12),4-diene (6.9%) and β-himachalene (2.3%) in the essential oil from the roots. While the essential oils did not show activity against tested bacterial strains, they both exhibited antifungal activity against C. albicans at varying concentrations, which was promising in the case of roots essential oils which exhibited an MIC of 250 μg/mL. Based on our findings, the topical use of F. xylorhachis roots essential oils could be considered an alternative therapy for superficial infections caused by Candida species.
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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".