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Effect of D‐amino acid contamination on isotope enrichment in plasma and urine.

2008· article· en· W2289586171 on OpenAlexafffund
Christopher Tomlinson, Mahroukh Rafii, Ronald O. Ball, Paul B. Pencharz

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsUniversity of AlbertaHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsIsotopomersUrineChemistryAmino acidIsotopeContaminationChromatographyStable isotope ratioGene isoformRadiochemistryBiochemistryBiologyOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

During stable isotope studies in enterally fed humans infants (n=7) using M+1 Phe and M+2 Arg, we found unexpectedly high levels of urinary enrichment, measured by tandem LC‐MS, of these amino acid isotopomers. Both isotopes had a D‐content of <0.2%. This led us to perform chiral LC column measurements to assess for D‐amino acid contamination. We found that on average 37% (range 30–50%) of total M+1 Phe was comprised of the D‐isoform. No D‐Phe was seen in plasma (n=4), and the enrichment of the L‐isoform was comparable in blood and urine. Similarly in urine (n=7) the D isoform of M+2 Arg comprised 82.5% (76–87), however, in plasma (n=4) the D‐content was significant comprising 22% (10–40%) of the total. We conclude that investigators using commercially produced, stable isotope labeled amino acids need to be aware that D‐contamination <0.2% may result in significant errors for enrichment, even in plasma, if chiral columns are not used. Supported by a grant from the CIHR.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.229
Teacher spread0.222 · 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

Citations1
Published2008
Admission routes2
Has abstractyes

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