Quantitative structural multiclass lipidomics using differential mobility: electron impact excitation of ions from organics (EIEIO) mass spectrometry
Bibliographic record
Abstract
We report a method for comprehensive structural characterization of lipids in animal tissues using a combination of differential ion mobility spectrometry (DMS) with electron-impact excitation of ions from organics (EIEIO) mass spectrometry. Singly charged lipid ions in protonated or sodiated forms were dissociated by an electron beam having a kinetic energy of 10 eV in a branched radio-frequency ion trap. We established a comprehensive set of diagnostics to characterize the structures of glycerophospholipids, sphingolipids, and acylglycerols, including glycosylated, plasmalogen, and ester forms. This EIEIO mass spectrometer was combined with DMS as a separation tool to analyze complex lipid extracts. Deuterated quantitative standards, which were added during extraction, allowed for the quantitative analysis of the lipid molecular species in various lipid classes. We applied this technique to the total lipids extracted from porcine brain, and we structurally characterized over 300 lipids (with the exception of cis / trans double-bond isomerism in the acyl chains). The structural dataset of the lipidomes, whose regioisomers were distinguished, exhibit a uniquely defined distribution of acyl chains within each lipid class; that is, sn -1 and sn -2 in the cases of glycerophospholipids or sn -2 and ( sn -1, sn -3) in the cases of triacylglycerols.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".