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Record W2757115775 · doi:10.1373/clinchem.2017.278457

Short-Term Stabilities of 21 Amino Acids in Dried Blood Spots

2017· letter· en· W2757115775 on OpenAlexafffund
Jun Han, Rehan Higgins, Mark D. Lim, Karen Lin, Juncong Yang, Christoph H. Borchers

Bibliographic record

VenueClinical Chemistry · 2017
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsMcGill UniversityJewish General HospitalGenome British ColumbiaUniversity of Victoria
FundersGenome AlbertaLeading Edge Endowment FundJewish General HospitalGenome British ColumbiaGenome CanadaBill and Melinda Gates Foundation
KeywordsTrifluoroacetic acidDried blood spotDried bloodChromatographyChemistryElectrospray ionizationAmino acidNewborn screeningMass spectrometryAcetonitrileSpotsSample preparationBiochemistry

Abstract

fetched live from OpenAlex

To the Editor: Dried blood spot (DBS)1 sampling is used in disease diagnosis, epidemiological surveillance, and newborn screening, and enables sample collection far from analytical laboratories and shipment via mail services. Amino acids (AAs) measured in DBS are routinely used for the diagnosis of inborn errors of metabolism (1) and childhood malnutrition (2), but environmental stresses incurred during sampling, transportation, and storage can impact the short-term (3) and long-term (4) stabilities of some AAs. Here, we systematically investigated the stabilities of 21 routinely analyzed AAs in DBS under environmental conditions simulating a global health work flow. DBSs were prepared by precisely blotting 30 μL of whole blood onto cellulose-based Whatman 903, FTA DMPK-C, and cotton-based PerkinElmer 226 cards. The optimized sample preparation used 200 μL of 0.02% trifluoroacetic acid in water to completely resuspend the entire DBS spot, followed by ultrasonic extraction with 800 μL of methanol/acetonitrile (1:1, v/v) containing 0.02% trifluoroacetic acid. The AAs were dansylated (5) at pH 9.2 and analyzed by C18 reversed-phase liquid chromatography/positive electrospray ionization/multiple-reaction monitoring mass spectrometry, with 21 13C- or 2H-labeled internal standards, for precise measurements of their molar concentrations (CVs ≤ 7.9% and recoveries of 88.4%–112.8%). Four sets of stability-testing experiments were conducted, and the molar …

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.327
Teacher spread0.294 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations34
Published2017
Admission routes2
Has abstractyes

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