Quantification of Pesticides and In Vitro Effects of Water-Soluble Fractions of Agricultural Soils in South Africa
Bibliographic record
Abstract
Abstract Although agrochemicals protect crops and reduce losses, these chemicals can migrate to non-target environments via run-off and leaching following irrigation or heavy rainfall, where non-target organisms can be exposed to a mixture of water-soluble compounds. This study investigated whether the water-soluble fractions of selected agricultural soils from South Africa contain quantifiable concentrations of four commonly used pesticides, 2,4-dichlorophenoxyacetic acid (2,4-D), atrazine, dicamba and imidacloprid, and whether the aqueous extracts induce effects in vitro. Effects investigated included cytotoxicity using the MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] cell viability assay; xenobiotic metabolism using the H4IIE- luc rat hepatoma cell line; and (anti-)androgenic and (anti-)oestrogenic effects were screened for with the human breast carcinoma cell lines MDA-kb2 and T47D-KB luc , respectively. Oxidative stress responses were measured in H4IIE- luc and human duodenum adenocarcinoma (HuTu-80) cells. All extracts of soil induced oxidative stress, while several samples caused moderate to severe cytotoxicity and/or anti-androgenic effects . The herbicide atrazine had the greatest frequency of detection (89%), followed by dicamba (84%), 2,4-D (74%) and imidacloprid (32%). Concentrations of atrazine [2.0 × 10 –1 to 2.1 × 10 2 ng/g, dry mass (dm)] and the neonicotinoid insecticide, imidacloprid (2.0 × 10 1 to 9.7 × 10 1 ng/g, dm), exceeded international soil quality guidelines. Overall, there was no observable trend between the biological effects and pesticides quantified. Nonetheless, the findings of this study show that agricultural soils in South Africa can elicit effects in vitro and contain quantifiable concentrations of polar pesticides. These agrochemicals might pose risks to the health of humans and the environment, but more assessment is necessary to quantify such potential effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".