Use of Lipidomics for Analyzing Glycerolipid and Cholesteryl Ester Oxidation by Gas Chromatography, HPLC, and On-line MS
Bibliographic record
Abstract
Various analytical techniques have been adopted for the isolation and identification of the oxolipids and for determining their functionality. Gas chromatography in combination with mass spectrometry (MS) has been specifically utilized in analysis of isoprostanes and other low molecular weight oxolipids, although it requires derivatization of the solutes. In contrast, liquid chromatography (LC) in combination with on-line MS has proven to be well suited for analysis of intact oxolipids without (or minimal) derivatization. LC-MS has also been helpful for the identification of lipidomic changes resulting from covalent binding of lipid ester core aldehydes to amino lipids, amino acids, peptides, and proteins. This chapter reviews the use of the above techniques for lipidomic analysis of the autoxidation products of cholesteryl esters and glycerolipids as practiced in the authors' laboratories.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".