Evaluation of herbicidal and fungicidal activities of six natural components and their phenoxyacetic acids derivatives and assessment of human health risk posed by oral exposure to them
Bibliographic record
Abstract
In the present study, we isolated linalool, geraniol, carvacrol, thymol, menthol and eugenol; synthesised their phenoxyacetic acid derivatives; and assessed their phytotoxic and genotoxic potentials against weeds and agricultural crops. Moreover, the fungicidal activity of both categories of compounds against 12 fungi was examined. Finally, using probabilistic assessment based on Risk Quotient (RQ) by the Monte Carlo simulation (MCS) method, we examined human health risks via oral exposure to these compounds. Toxic potencies to plants demonstrated that all parameters and related indices including germination percentage, mean germination time, germination rate, root length, shoot length, seedling length and vigour index dose-dependently decreased in both weeds and agricultural crops. Based on the comet assay, DNA damage was observed in the root meristems of the plants. Consequently, a significant decrease was observed in head DNA, whereas a significant increase in tail DNA was observed in response to treatments. Besides, the natural components and semisynthetic molecules showed a broad spectrum of fungicidal traits. However, eugenol and eugenoxyacetic acid inhibitory effects on the growth of the tested fungi were similar to that of benomyl. Our probabilistic health risk assessment indicated di minimis risk for natural components and their phenoxyacetic acid derivatives. Sensitivity analysis showed that concentrations of residues of the compounds had the most significant effect on risk. Current findings could offer a platform for developing sustainable approaches to reduce chemical residues and their consequent risks to the environment and human health.
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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) | 0.000 | 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".