Understanding the Fate of Environmental Chemicals Inside Living Organisms: NMR-Based <sup>13</sup>C Isotopic Suppression Selects Only the Molecule of Interest within <sup>13</sup>C-Enriched Organisms
Bibliographic record
Abstract
In vivo nuclear magnetic resonance (NMR) is rapidly evolving as a critical tool as it offers real-time metabolic information, which is crucial for delineating complex toxic response pathways in living systems. Organisms such as Daphnia magna (water fleas) and Hyalella azteca (freshwater shrimps) are commonly 13 C-enriched to increase the signal in NMR experiments. A key goal of in vivo NMR is to monitor how molecules (nutrients, contaminants, or drugs) are metabolized. Conventionally, these studies would normally involve using a 13 C-enriched probe molecule and feeding this to an organism at natural abundance, in turn allowing the fate of the probe molecule to be selectively analyzed. The drawback of such an approach is that there is a limited range of 13 C-enriched probe molecules, and if available, they are extremely cost prohibitive. Uniquely, when utilizing 13 C organisms, a reverse strategy of isotopic filtering becomes possible. The concept described here uses 1 H detection in combination with a 13 C filter on living organisms. The purpose is to suppress all 1 H signals from the organism (i.e., 1 H attached to 13 C), leaving only the probe molecule ( 1 H attached to 12 C). Because the probe molecule can be selectively observed using this approach, it then makes it possible to follow and discern processes such as bioconversion, bioaccumulation, and excretion in vivo. As the approach uses 1 H detection, it provides excellent detection limits in the nanogram range. In this article, the approach is introduced, optimized on standards, and then applied to follow nicotine biotransformation and lipid assimilation in vivo to demonstrate the concept.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".