Quantitative metabolic profiling of urinary eicosanoids for clinical phenotyping
Bibliographic record
Abstract
The eicosanoids are a family of lipid mediators of pain and inflammation involved in multiple pathologies, including asthma, hypertension, cancer, atherosclerosis, and neurodegenerative diseases. These signaling mediators act locally, but are rapidly metabolized and transported to the systemic circulation as a mixture of primary and secondary metabolites. Accordingly, urine has become a useful readily accessible biofluid for monitoring the endogenous synthesis of these molecules. Herein, we present the validation of a rapid, repeatable, and precise method for the extraction and quantification of 32 eicosanoid urinary metabolites by LC-MS/MS. For 12 out of 17 deconjugated glucuronide eicosanoids, there was no improvement in recovered signal. These metabolites cover the major synthetic pathways, including prostaglandins, leukotrienes, and isoprostanes. The method linearity was >0.99 for all metabolites analyzed, the limit of detection ranged from 0.05–5 ng/ml, and the average extraction recoveries were >90%. All analytes were stable for at least three freeze/thaw cycles. The method was formatted for large-scale analysis of clinical cohorts, and the long-term repeatability was demonstrated over 15 months of acquisition, evidencing high precision (CV <15%, except for tetranorPGEM and 2,3-dinor-11α-PGF 2α , which were <30%). The presented method is suitable for focused mechanistic studies as well as large-scale clinical and epidemiological studies that require repeatable methods capable of producing data that can be concatenated across multiple cohorts.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 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".