Improvements in lipid suppression for <sup>1</sup>H NMR‐based metabolomics: Applications to solution‐state and HR‐MAS NMR in natural and in vivo samples
Bibliographic record
Abstract
Proton nuclear magnetic resonance (NMR) spectra of intact biological samples often show strong contributions from lipids, which overlap with signals of interest from small metabolites. Pioneering work by Diserens et al. demonstrated that the relative differences in diffusivity and relaxation of lipids versus small metabolites could be exploited to suppress lipid signals, in high-resolution magic angle spinning (HR-MAS) NMR spectroscopy. In solution-state NMR, suspended samples can exhibit very broad water signals, which are challenging to suppress. Here, improved water suppression is incorporated into the sequence, and the Carr-Purcell-Meiboom-Gill sequence (CPMG) train is replaced with a low-power adiabatic spinlock that reduces heating and spectral artefacts seen with longer CPMG filters. The result is a robust sequence that works well in both HR-MAS as well as static solution-state samples. Applications are also extended to include in vivo organisms. For solution-state NMR, samples containing significant amount of fats such as milk and hemp hearts seeds are used to demonstrate the technique. For HR-MAS, living earthworms (Eisenia fetida) and freshwater shrimp (Hyalella azteca) are used for in vivo applications. Lipid suppression techniques are essential for non-invasive NMR-based analysis of biological samples with a high-lipid content and adds to the suite of experiments advantageous for in vivo environmental metabolomics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".