The n-3 PUFA content of the global lipidomes of NIST SRM 2378, SRM 1950, and intralaboratory quality control materials
Bibliographic record
Abstract
The regular use of commercially available Standard Reference Materials (SRM) and intralaboratory quality control materials, which are important tools, is essential for standardizing and ensuring high-quality lipidomic analyses. These materials should also be relevant for the research application. To support nutritional lipidomic research, the global lipidome of materials derived from individuals consuming a range of n-3 PUFAs was determined. Nontargeted lipidomics were completed on SRM 2378 (serum), SRM 1950 (plasma), and intralaboratory quality control (plasma) generated from individuals with low omega-3 and high omega-3 status. SRM 2378 includes materials generated from individuals consuming fish oil (SRM 2378-1), flaxseed oil (SRM 2378-2), and no supplements (SRM 2378-3). Specific lipids with differences were identified using fold-based and absolute differences in semiquantitated concentrations. Individual lipids containing 20:5 and 22:6 were highly variable and largely reflected ad hoc intake estimates of EPA and DHA. Fold-based approaches identified low-abundant lipids that differed, whereas absolute differences identified high-abundant lipid species that differed. In addition, differences due to dietary fatty acid intakes were more dramatic than differences between serum and plasma in these nontargeted analyses. The dietary intake of EPA and DHA can impact lipidomic profiles, which should be considered by lipidomic analysts. These results also suggest that comprehensive dietary assessments should be considered during the development of reference and quality control materials.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".