Organosulphur constituents in garlic oil elicit antennal and behavioural responses from the yellow fever mosquito
Bibliographic record
Abstract
Garlic (Allium sativum) and its essential oil have long been used for their distinct flavour, therapeutic effects and as a topical and systemic insect repellent. We tested the hypothesis that the yellow fever mosquito, Aedes aegypti L. (Diptera: Culicidae), responds electrophysiologically and behaviourally to specific components of the steam-distilled essential oil of garlic. In coupled gas chromatographic-electroantennographic detection analyses of garlic oil, antennae of female Ae. aegypti responded to 14 compounds. Seven of them [diallyl disulphide, diallyl trisulphide, diallyl tetrasulphide, 2-(2,3-dithia-5-hexenyl)-3,4-dihydro-2H-thiopyran, 3-(2,3-dithia-5-hexenyl)-3,4-dihydro-2H-thiopyran, 6-methyl-4,5,8,9-tetrathiadodeca-1,11-diene and 4,5,9,10-tetrathiatrideca-1,12-diene] were isolated or synthesized and tested for their ability to repel host-seeking female Ae. aegypti. A solution of diallyl trisulphide and diallyl tetrasulphide applied to a human forearm provided protection from female mosquitoes significantly longer than the paraffin oil control. All compounds had mean protection times significantly shorter than an equivalent dose of the ‘gold standard’N,N-diethyl-3-methylbenzamide. Understanding the common moiety in organosulfur compounds that causes repellence could lead to the design of analogues that are more effective than their natural counterparts in repelling mosquitoes.
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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".