Identification of lysophosphatidylcholine (LPC) and platelet activating factor (PAF) from PC12 cells and mouse cortex using liquid chromatography/multi‐stage mass spectrometry (LC/MS <sup>3</sup> )
Bibliographic record
Abstract
Lipids play essential roles in cellular structural support, energy storage and signal transduction. Recently, mass spectrometry (MS) has been used to produce three-dimensional maps that elucidate the lipid composition of complex cellular lysates. The identification of individual lipids within these maps is slow and requires the synthesis and spiking of each candidate lipid. We present a novel MS-based technique that rapidly elucidates the atomic connectivity of the fatty acid/alcohol substituent on the sn-1 position of several different families of glycerophosphocholine-containing lipids within the confines of a chromatographic separation. Sodiated lipid species were fragmented to produce radical cations which lost successive methylene groups upon further collisional activation to reveal the identity of the parent molecule. This approach was demonstrated to be effective on isobaric members of the lysophosphatidylcholine (LPC) and platelet activating factor (PAF) families of glycerophospholipids. We demonstrate the application of this technique to unambiguously identify these species within complex cellular lysates and tissue extracts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".