Development of a Rapid Ultra High‐Performance Liquid Chromatography/Tandem Mass Spectrometry Method for the Analysis of <i>sn</i>‐1 and <i>sn</i>‐2 Lysophosphatidic Acid Regioisomers in Mouse Plasma
Bibliographic record
Abstract
Lysophosphatidic acids (lysoPtdOH) are involved in several physiological processes including cell proliferation, inflammation, and glucose metabolism. However, measuring lysoPtdOH is challenging due to inadequate extraction techniques, poor chromatographic resolution, or the inability to discriminate between sn-1 and sn-2 regioisomers. In the present work, we developed a high-throughput (10 min run times) ultra-high-performance liquid chromatography-tandem mass spectrometry method capable of discriminating lysoPtdOH species by their fatty acyl composition and sn-localization on glycerol backbones. We quantitated sn-1/sn-2 regioisomeric pairs of lysoPtdOH with 16:0, 18:0, 18:1, 18:2, 20:4, and 22:6 fatty acyl chains using 50 μL of mouse plasma. The method presented here can be expanded to profile more lysoPtdOH species, and has the potential to be used in clinical settings to quickly screen lysoPtdOH profiles. Finally, the ability to discriminate between sn-1 and sn-2 isomers can provide insights regarding the metabolic origins and fates of specific lysoPtdOH molecules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".