Fatty Acyl-Specific Macrolipidomics and Microlipidomics for Nutritional Research
Bibliographic record
Abstract
The field of lipidomics can further our understanding of the biochemical processes of human health and disease. Generally, lipidomics methods utilize high-performance liquid chromatography (HPLC) for lipid separation, followed by detection using tandem mass spectrometry (MS/MS). The joint use of HPLC and MS/MS has increased dramatically over the past few years, and novel technologies continue to increase the versatility and practical usability of various lipidomics methods. However, a lack of harmonized language, nomenclature and standardized analytical strategies can result in lipid misannotations, improper analyte identifications, and incorrect quantitative results. In this thesis, the importance of adopting appropriate analytical strategies to answer research hypothesis(es) will be highlighted. Specifically, this entails a comparison between analytical platforms and four HPLC-MS/MS data acquisition strategies for untargeted/global lipidomic profiling of highly-abundant lipids including phospholipids, triacylglycerols and cholesteryl esters in human whole blood. In addition, the advantages of targeted analytical approaches for the measurement of specific lipid classes will be examined through the development of a tailored method for the determination of regioisomers of lysophosphatidic acid in plasma (mouse), the acyl species of triacylglycerols in cooking oil (sunflower), and the acyl species of phospholipids in brain tissue (mouse). Finally, comprehensive profiling of various lipid classes in whole blood using a novel retention time-based negative/positive ion mode switching method will be used to screen for potential blood biomarkers of omega-3 polyunsaturated fatty acid intake. This will include samples from a cross-sectional dietary assessment study in humans, and an acute/chronic docosahexaenoic acid supplementation study in rats. The methods presented in this thesis have the potential to be expanded for use in agriculture, nutrition, research and clinical settings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".