Protective antifungal activity of essential oils extracted from <i>Buddleja perfoliata</i> and <i>Pelargonium graveolens</i> against fungi isolated from stored grains
Bibliographic record
Abstract
AIMS: The chemical composition and antifungal activity of essential oils extracted from Buddleja perfoliata and Pelargonium graveolens were analysed to assess their efficacy as a potential alternative to synthetic chemical fungicides to protect stored grain. METHODS AND RESULTS: Essential oils were obtained by hydrodistillation, while GC-MS were used to characterize the components of theses oils. The main components identified from the essential oil of B. perfoliata were cubenol, eudesmol, germacrene D-4-ol and cis-verbenol; whereas (-)-aristolene, β-citronellol and geraniol, were identified in P. graveolens. These essential oils were tested against a panel of fungal strains isolated from stored grains. Toxicity of the essential oils was assessed using two models represented by human-derived macrophages and the brine shrimp assay. Moreover, inflammatory response of the oils was assessed by measuring secretion of the pro-inflammatory cytokines IL-6 and TNF-α using a human-derived macrophage cell line. Results show potent antifungal activity against a collection of fungi, with minimal inhibitory concentrations ranging from 0·3 to 50 μg ml(-1) for both plants. A moderated cytotoxicity was observed, but no inflammatory responses. CONCLUSIONS: These oils can be used as an alternative for synthetic chemical fungicides used to protect stored grains. SIGNIFICANCE AND IMPACT OF THE STUDY: Synthetic chemical fungicides are used to protect stored grains, but their broad use raises concerns about effects on the environment and human health. The impact of the present report is that the use of essential oils is an eco-friendly alternative for fungal control in postharvest grains with a low impact to the environment.
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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".