Effect of J‐coupling on lipid composition determination with localized proton magnetic resonance spectroscopy at 9.4 T
Bibliographic record
Abstract
PURPOSE: To demonstrate, at 9.4 T, that J-coupling interactions exhibited by lipid protons affects lipid composition determination with a point resolved spectroscopy (PRESS) sequence. MATERIALS AND METHODS: Experiments were conducted on four oils (almond, corn, sesame, and sunflower), on visceral adipose tissue of a euthanized mouse, and on pure linoleic acid at 9.4 T. The 2.1, 2.3, and 2.8 ppm resonances were measured at multiple echo times (TEs) by a standard PRESS sequence and by a PRESS sequence consisting of narrow-bandwidth refocusing pulses designed to rewind the J-coupling evolution of the target peak protons in the voxel of interest. T(2) corrections were performed on both groups of data for the three peaks and lipid compositions for the oils and for the mouse tissue were determined. Lipid compositions were also calculated from a short-TE standard PRESS spectrum. RESULTS: A chemical analysis of the samples was not performed; however, the oil compositions calculated from resonance peaks acquired with the PRESS sequence designed to minimize J-coupling effects, following T(2) relaxation correction, closely agreed with values in the literature, which was not the case for all of the compositions determined from the regular PRESS spectra. CONCLUSION: The presented work brings to attention the significance of J-coupling effects when calculating lipid compositions from localized proton spectra.
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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 | 0.003 |
| 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 | 0.000 | 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".