Improved metabolite characterization by liquid chromatography – Tandem mass spectrometry through electron impact type fragments from adduct ions
Bibliographic record
Abstract
Using a chimeric collision cell mounted on a quadrupole time-of-flight platform, collision induced dissociation (CID) and electron induced dissociation (EID) were investigated for the LC-MS analysis of low molecular weight compounds including drugs and endogenous metabolites. Compared to CID, EID fragmentation of the [M+H]+ species (10–20 eV) from standard compounds resulted in additional specific and informative fragments, mostly due to neutral losses and, in some cases due to ring openings. Some analytes, for example reserpine and vinpocetine, provided characteristic [M+H]•2+ species. For most analytes for sodium and potassium adducts and multimers a radical cation M•+ and electron impact type fragments were observed in the EID spectra, providing the opportunity to use EI libraries to support metabolite identification. EID opens the possibility to get structural information from adduct ions which is often not the case with CID. EID enabled the putative characterization of two metabolites in rat urine as glucuronides of 5,6-dihydroxyindole based on EID fragmentation of the potassium adducts.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".