Glucosinolate Profiling and Antimicrobial Screening of <i>Aurinia leucadea</i> (Brassicaceae)
Bibliographic record
Abstract
Glucosinolates (GLs) were characterized in various aerial parts (stems, leaves, and flowers) of Aurinia leucadea (Guss.) C. Koch and quantified according to the ISO 9167-1 official method based on the HPLC analysis of desulfoglucosinolates. Eight GLs, i.e., glucoraphanin (GRA), glucoalyssin (GAL; 1), gluconapin (GNA; 2), glucocochlearin (GCC), glucobrassicanapin (GBN; 3), glucotropaeolin (GTL), glucoerucin (GER), and glucoberteroin (GBE) were identified. The total GL contents were 57.1, 37.8, and 81.3 μmol/g dry weight in the stems, leaves, and flowers, respectively. The major GL detected in all parts of the plant was 2, followed by 1 and 3. GC/MS Analysis of the volatile fractions extracted from the aerial parts of fresh plant material either by hydrodistillation or CH(2) Cl(2) extraction showed that these fractions mostly contained isothiocyanates (ITCs). The main ITCs were but-3-enyl- (55.6-71.8%), pent-4-enyl- (7.6-15.3%), and 5-(methylsulfinyl)pentyl ITC (0-9.5%), originating from the corresponding GLs 2, 3, and 1, respectively. The antimicrobial activity of the volatile samples was investigated by determining inhibition zones with the disk-diffusion method and minimal inhibitory concentrations (MIC) with the microdilution method. They were found to inhibit a wide range of bacteria and fungi, with MIC values of 2.0-32.0 μg/ml, indicating their promising antimicrobial potential, especially against the fungi Candida albicans and Rhizopus stolonifer as well as against the clinically important pathogen Pseudomonas aeruginosa.
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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) | 0.000 | 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".