Phospholipids of Plasma Lipoproteins, Red Blood Cells and Atheroma, Analysis of
Bibliographic record
Abstract
Abstract The large number of lipid classes and great complexity of molecular species present in blood plasma, red cells, platelets and atheromatous lesions requires a combination of analytical techniques for comprehensive analyses, including chemical and enzymatic derivatization of samples. In many instances only partial analyses are required, which can be accomplished by specific analytical techniques. This chapter describes the full spectrum of the methodology ranging from the most basic thin‐layer chromatography (TLC) to the detailed mass spectrometric assays. Traditional sample extraction by liquid–liquid partition is time‐consuming and involves large volumes of solvents. Liquid–solid extraction using adsorbent cartridges is more economical. At the present time total lipid extracts can be effectively assayed for lipid class content and molecular species composition by flow injection tandem mass spectrometry (MS/MS), while a more detailed analysis of complex lipid mixtures is provided by a combination of liquid chromatography with on‐line electrospray mass spectrometry (LC/ESMS). The new techniques permit completion of the analyses in a few hours, where previously several days or weeks may have been required. The soft ionization mass spectrometric techniques have permitted the recognition and detailed analysis of such minor components of blood as the lipid oxidation and glycation products often observed in disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".