Isotope-labeling derivatization with 3-nitrophenylhydrazine for LC/multiple-reaction monitoring-mass-spectrometry-based quantitation of carnitines in dried blood spots
Bibliographic record
Abstract
-labeled derivatives of the mono- and di-carboxylic carnitines in a "one-pot" reaction. These labeled analogues were used as stable isotope-labeled internal standards to compensate for possible ESI matrix effects. Combined with an optimized, two-step procedure for the extraction of carnitines from DBS, this isotope-labeling derivatizaiton - LC/MRM-MS method provided good linearity, high precision (intra-day CVs of ≤7.8% and inter-day CVs of ≤8.8%) and high accuracy (three levels of standard substances spiked in, with recoveries of 86.9%-109.7%) quantitation of carnitines in three sets of DBSs on cellulose or cotton filter paper. This method was then applied to determine the concentration changes of the analytes in the DBSs under two stability-testing regimes: 1) a one-time 4-h sunlight exposure and 2) a set of cycled temperature transitions (-20 °C for 2 days, 40 °C for 2 days, and back to -20 °C for 2 additional days). All of the carnitines showed good stabilities under the first testing condition. Under the second testing condition, free carnitine showed concentration increases of 9.3%-16.1%; acetyl carnitine, 3-OH butyryl carnitine, and malonyl carnitine showed concentration decreases of 12.2%-17.3%, 12.9%-17.1% and 10.7%-15.3%, respectively, and other 20 acyl carnitines showed concentration changes of <10% in three sets of DBSs on cellulose or cotton filter paper. These preliminary stability-testing results indicate a need to more systematically investigate the effects of various environmental conditions on the chemical stabilities of carnitines in DBS specimens if this sampling method is to be used in remote health applications.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".