Sensitive and Comprehensive Lipid Mediator Analysis using Advanced Scheduled MRM with Polarity Switching and QTRAP Enhanced Product Ion Scanning
Bibliographic record
Abstract
Using a QTRAP® 6500+ system coupled with an ExionLC™ System, we developed a targeted panel of 88 compounds for comprehensive profiling of lipid mediators and pathway markers. A Kinetex® Polar C18 column was used for LC based separation of lipid mediators, epimers and other isoelemental structures within a 20 min run time. Advanced scheduled sMRM was used to optimize scanning windows and dwell weighting. The negative mode lipid mediator panel includes pro‐inflammatory prostaglandins, leukotrienes and their primary metabolites, specialized pro‐resolving mediators (SPM) including resolvins, protectins, maresins, and lipoxins, biomarkers of ROS and NOS, and fatty acid and mono‐hydroxy fatty acid precursors. The positive mode panel includes cysteinyl leukotrienes, PAF, and maresin and protectin conjugates in tissue regeneration (MCTRs and PCTRs). Concentration curves were generated by injecting 3 replicate injections of 8 different concentrations of each lipid mediator standard. Excellent linearity is observed with r 2 of at least 0.997 for all analytes. Excellent sensitivity was obtained for all lipid mediator species with LLOQ between 0.05 and 1 ng/mL and CV < 30%. The unique qualitative/quantitative QTRAP platform also enabled MRM triggered EPI experiments which provided high sensitivity MS/MS data for confirmation of low level analytes. Baseline separation of LXA 4 and 15‐epi‐LXA 4 well as RvD1 and 17‐epi‐RvD1 enables the differentiation of enzymatic pathway utilization for pro‐resolving lipid mediator production.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".