Resolving the omega‐3 methyl resonance with long echo time magnetic resonance spectroscopy in mouse adipose tissue at 9.4 T
Bibliographic record
Abstract
Tissue omega‐3 (ω‐3) content is biologically important to disease; however, its quantification with magnetic resonance spectroscopy in vivo is challenging due to its low concentration. In addition, the ω‐3 methyl resonance (≈ 0.98 ppm) overlaps that of the non‐ω‐3 (≈ 0.90 ppm), even at 9.4 T. We demonstrate that a Point‐RESolved Spectroscopy (PRESS) sequence with an echo time (TE) of 109 ms resolves the ω‐3 and non‐ω‐3 methyl peaks at 9.4 T. Sequence efficacy was verified on five oils with differing ω‐3 fat content; the ω‐3 content obtained correlated with that measured using 16.5 T NMR (R2 = 0.97). The PRESS sequence was also applied to measure ω‐3 content in visceral adipose tissue of three different groups (all n = 3) of mice, each of which were fed a different 20% w/w fat diet. The fat portion of the diet consisted of low (1.4%), medium (9.0%) or high (16.4%) ω‐3 fat. The sequence was also applied to a control mouse fed a standard chow diet (5.6% w/w fat, which was 5.9% ω‐3). Gas chromatography (GC) analysis of excised tissue was performed for each mouse. The ω‐3 fat content obtained with the PRESS sequence correlated with the GC measures (R2 = 0.96). Apparent T2 times of methyl protons were assessed by obtaining spectra from the oils and another group of four mice (fed the high ω‐3 diet) with TE values of 109 and 399 ms. Peak areas were fit to a mono‐exponentially decaying function and the apparent T2 values of the ω‐3 and non‐ω‐3 methyl protons were 906 ± 148 and 398 ± 78 ms, respectively, in the oils. In mice, the values were 410 ± 68 and 283 ± 57 ms for ω‐3 and non‐ω‐3 fats, respectively.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".