A new derivatization method coupled with <scp>LC</scp>‐<scp>MS</scp>/<scp>MS</scp> to enable baseline separation and quantification of dimethylarginines in human plasma from patients to receive on‐pump <scp>CABG</scp> surgery
Bibliographic record
Abstract
Asymmetric dimethylarginine (ADMA) is an inhibitor of nitric oxide synthase and a risk factor for cardiovascular events. We have developed a new derivatization method to enable baseline separation of the regio-isomers, ADMA, and symmetric dimethylarginine (SDMA), within 15 min on a C18 reverse phase column. Reacting naphthalene-2,3-dicarboxaldehyde with ADMA and SDMA in the presence of 2-mercaptoethanol produces corresponding 2,3-dihydro-benzo[f]isoindol-1-ones that are more stable than previously reported ortho-phthaldialdehyde and 2-mercaptoethanol derivatives. LC-MS/MS quantitation of these derivatives can be used to determine ADMA and SDMA concentrations in the plasma of patients to receive on-pump coronary artery bypass grafting (CABG) surgery. The LOD, LOQ and lower LOQ (LLOQ) of this method were determined to be 2.6, 8.7, and 25 nM for ADMA, and 2.5, 8.3, and 25 nM for SDMA, respectively, with consumption of only 50 μL of plasma. The relative standard deviations and relative errors of the intraday and interday determinations, as measurements of reproducibility and accuracy, are all within 15%. The ADMA and SDMA concentrations in patient plasma are 298.1 ± 11.2 nM (mean ± S.E.M., n = 123) and 457.7 ± 19.8 nM (mean ± S.E.M., n = 123), respectively. Upon unblinding of our clinical trial, these predetermined values might explain patient clinical outcomes associated with on-pump CABG surgery, as ADMA is known to inhibit nitric oxide production. Furthermore, this derivatization reaction in conjunction with LC-MS/MS analysis may open a venue to explore alternative chemical labeling modes for LC-MS/MS applications, such as analysis of other amino acids, metabolites, and peptides containing primary amine group(s).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".