1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal trifluralin and endosulfan exposure
Bibliographic record
Abstract
Environmental context Environmental metabolomics is an emerging field that examines the metabolic changes in organisms in response to potential environmental stressors. In this study, nuclear magnetic resonance spectroscopy is used to investigate earthworm metabolic responses to sub-lethal exposure of environmentally persistent pesticides. The study identifies two toxic modes of action elicited by the pesticides, and highlights the potential of metabolomics for the chemical assessment of persistent environmental contaminants. Abstract 1-D and 2-D nuclear magnetic resonance (NMR) spectroscopy is used to examine the metabolic response of the earthworm (Eisenia fetida) after contact test exposure to an organofluorine pesticide, trifluralin, and an organochlorine pesticide, endosulfan. Three sub-lethal concentrations were used for each pesticide (0.1, 0.5 and 1.0 mg cm–2 for trifluralin and 0.5, 1.0 and 2.0 μg cm–2 for endosulfan). Principal component analysis of the trifluralin and endosulfan NMR datasets showed separation between the unexposed and the exposed earthworm groups. Alanine, glycine, maltose and ATP were significant in the highest concentration (1.0 mg cm–2) for trifluralin-exposed earthworms and may result from a non-polar narcosis toxic mode of action (MOA). Leucine, phenylalanine, tryptophan, lysine, glutamate, valine, glycine, isoleucine, methionine, glutamine, alanine, maltose, glucose, meibiose, malate, fumarate and ATP were detected as significant for the two highest concentrations (1.0 and 2.0 μg cm–2) for endosulfan-exposed earthworms and a neurotoxic MOA is postulated. This study highlights the use of 1-D and 2-D metabolomics for understanding the biochemical response of environmental contaminants to model organisms such as earthworms.
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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".