MétaCan
Menu
Back to cohort
Record W2792502638 · doi:10.1016/j.aca.2018.01.045

Isotope-labeling derivatization with 3-nitrophenylhydrazine for LC/multiple-reaction monitoring-mass-spectrometry-based quantitation of carnitines in dried blood spots

2018· article· en· W2792502638 on OpenAlexafffund
Jun Han, Rehan Higgins, Mark D. Lim, Kieran Atkinson, Juncong Yang, Karen Lin, Christoph H. Borchers

Bibliographic record

VenueAnalytica Chimica Acta · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsJewish General HospitalMcGill UniversityGenome British ColumbiaUniversity of Victoria
FundersWarren Y. Soper Charitable TrustFondation De Famille Alvin SegalGenome AlbertaGenome British ColumbiaMcGill UniversityJewish General HospitalLeading Edge Endowment FundGenome CanadaBill and Melinda Gates Foundation
KeywordsChemistryChromatographyDerivatizationCarnitineReagentAnalyteSelected reaction monitoringMass spectrometryAcetylcarnitineTandem mass spectrometryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Carnitines are diagnostic biomarkers of fatty acid oxidation defects and organic acidemias. Quantitative measurements of various carnitines in dried blood spot (DBS) have potential use in remote health applications for disease diagnosis and epidemiological surveillance. To provide an improved LC/multiple-reaction monitoring (MRM)-MS method for quantitation of carnitines in DBS, 3-nitrophenylhydrazine (3NPH) was tested as a high-efficiency chemical isotope-labeling reagent for pre-analytical derivatization of 24 routinely-analyzed species. Reaction conditions were optimized and carnitine structural isomers were separated by reversed-phase LC with positive-ion MRM/MS detection, giving on-column lower LOQs of sub- to low-femtomole levels. 13 C 6 -3NPH was used to produce 13 C 6 - or 13 C 12 -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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.012
GPT teacher head0.257
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueAnalytica Chimica ActaSame topicMetabolism and Genetic DisordersFrench-language works237,207