Multiplexed Dilute-and-Shoot Liquid Chromatography–Multiple-Reaction Monitoring Mass Spectrometry Clinical Assay for Metanephrines and Catecholamines in Human Urine
Bibliographic record
Abstract
Background: Quantifying urinary catecholamines and metanephrines is essential for the clinical screening and diagnosis of neuroendocrine tumours. HPLC with electrochemical detection (HPLC-ECD) is commonly used for this type of analysis but requires extensive sample cleanup. Simple and rapid dilute-and-shoot LC–multiple-reaction monitoring (MRM)-MS assays have been developed for quantitating these analytes in urine but have not yet been validated according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. Methods: A simple dilute-and-shoot sample preparation without derivatization was used. C18 RP-UPLC-MRM-MS and positive-ion ESI were used, usually with two transitions per analyte being monitored. Certified deuterated internal standards were used for each analyte. Results: This assay was validated according to the CLSI C62-A guidelines, including accuracy/trueness, imprecision, sensitivity, specificity, carryover, stability, and linearity. The final MRM-MS method was compared to the established HPLC-ECD clinical chemistry reference method. The run time was reduced from 25 min to 5 min. Conclusions: A simple, robust, rapid, and cost-effective LC-MRM-MS assay for measuring urinary catecholamines and metanephrines was developed and validated according to the CLSI guidelines. This validated method requires minimal sample manipulation before analysis and provides sensitivity, specificity, and improved precision. The implementation of this assay in clinical laboratories will facilitate early and accurate diagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".