Tailoring <sup>1</sup>H Spin Dynamics in Small Molecules via Supercooled Water: A Promising Approach for Metabolite Identification and Validation
Bibliographic record
Abstract
Metabolic mixtures are often analyzed via NMR spectroscopy as it provides a metabolic profile without sample alteration in a noninvasive manner. These mixtures however tend to be very complex and demonstrate considerable spectral overlap resulting in assignments that are sometimes ambiguous given the range of current NMR methods available. De novo molecular identification in these mixtures is generally accomplished using chemical shift information and J-coupling based experiments to determine spin connectivity information, but these techniques fall short when a molecule of interest contains nonrelaying centers. A method is presented here that enhances intramolecular spatial interactions via supercooled water and uses the resulting spatial correlations to edit mixtures. This is accomplished by utilizing nuclear Overhauser effect spectroscopy (NOESY) at subzero temperatures in capillaries to enhance NOE and provide more complete spin systems. This technique is applied to a standard mixture of three known molecules in D(2)O with overlapping resonances and is further demonstrated to assign molecules in a worm tissue extract. The current method proves to be a powerful complement to existing methods such as total correlation spectroscopy (TOCSY) to expand the range of molecules that can be assigned in situ without physical separation of mixtures.
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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 | 0.001 |
| 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 | 0.000 | 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".