Comparison of 1-D and 2-D NMR techniques for screening earthworm responses to sub-lethal endosulfan exposure
Bibliographic record
Abstract
Environmental context The application of metabolomics from an environmental perspective depends on the analytical ability to discriminate minute changes in the organism resulting from exposure. In this study, 1-D and 2-D Nuclear Magnetic Resonance (NMR) experiments were examined to characterise the earthworm’s metabolic response to an organochlorine pesticide. 2-D NMR showed considerable improvement in discriminating exposed worms from controls and in identifying the metabolites responsible. This study demonstrates the potential of 2-D NMR in understanding subtle biochemical responses resulting from environmental exposure. Abstract Nuclear Magnetic Resonance (NMR) based metabolomics is a powerful approach to monitoring an organism’s metabolic response to environmental exposure. However, the discrimination between exposed and control groups, depends largely on the NMR technique chosen. Here, three 1-D NMR and three 2-D NMR techniques were investigated for their ability to discriminate between control earthworms (Eisenia fetida) and those exposed to a sub-lethal concentration of a commonly occurring organochlorine pesticide, endosulfan. Partial least-squares discriminant analysis found 1H–13C Heteronuclear Single Quantum Coherence (HSQC) spectroscopy to have the highest discrimination with a MANOVA value (degree of separation) three orders lower than any of the 1-D and 2-D NMR techniques. HSQC spectroscopy identified alanine, leucine, lysine, glutamate, glucose and maltose as the major metabolites of exposure to endosulfan, more than all the other techniques combined. HSQC spectroscopy in combination with a shorter 1-D experiment may prove to be an effective tool for the discrimination and identification of significant metabolites in organisms under environmental stress.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".