Bibliographic record
Abstract
Although direct mass spectrometry is capable of identification the major molecular species of lipids in crude total lipid extracts, prior chromatographic isolation is necessary for detection and identification of the minor components. This is especially important for the analysis of the oxolipids, which usually occur in trace amounts in the total lipid extract, and require prior isolation for detailed analysis. Both thin-layer chromatography and adsorption cartridges provide effective means for isolation and enrichment of lipid classes, while gas-liquid chromatography and high performance liquid chromatography with on-line mass spectrometry permit further separation and identification of molecular species. Prior chromatographic resolution is absolutely necessary for the identification of isobaric and chiral molecules, which mass spectrometry/mass spectrometry (MS/MS) cannot distinguish. Both gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry applications may require the preparation of derivatives in order to improve the chromatographic and mass spectrometric properties of the oxolipids which is a small inconvenience for securing analytical reliability. The following chapter reviews the advantages and necessity of combined chromatographic-mass spectrometric approaches to successful identification and quantification of molecular species of oxoacylglycerols and oxocholesteryl esters in in-vitro model studies of lipid peroxidation and in the analyses of oxolipids recovered from tissues.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".