A holistic NMR framework to understand environmental impact: Examining the impacts of superparamagnetic iron oxide nanoparticles (SPIONs) in <scp><i>Daphnia magna</i></scp> via imaging, spectroscopy, and metabolomics
Bibliographic record
Abstract
Superparamagnetic iron oxide nanoparticles (SPIONs) are a contaminant of emerging interest, often used in the medical field as an imaging contrast agent, with additional uses in wastewater treatment and as food additives. Although the use of SPIONs is increasing, little research has been conducted on the toxic impacts to living organisms beyond traditional lethal concentration endpoints. Daphnia magna are model organisms for aquatic toxicity testing with a well understood metabolome and high sensitivity to SPIONs. Thus, as environmental concentrations continue to increase, it is becoming critical to understand their sub-lethal toxicity. Due to the paramagnetic nature of SPIONs, a range of potential nuclear magnetic resonance spectroscopy (NMR) experiments are possible, offering the potential to probe the physical location (via imaging), binding (via relaxation weighted spectroscopy), and the biochemical pathways impacted (via in vivo metabolomics). Results indicate binding to carbohydrates, likely chitin in the exoskeleton, along with a decrease in energy metabolites and specific biomarkers of oxidative stress. The holistic NMR framework used here helps provide a more comprehensive understanding of SPIONs impacts on D. magna and showcases NMR's versatility in providing physical, chemical, and biochemical insights.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".