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Record W2097851755 · doi:10.1038/clpt.2013.114

CYP2A6 Genotype but not Age Determines Cotinine Half-Life in Infants and Children

2013· article· en· W2097851755 on OpenAlexafffund
Delia Dempsey, Nancy C. Sambol, Peyton Jacob, Ewa Hoffmann, Rachel F. Tyndale, Elena Fuentes‐Afflick, Neal L. Benowitz

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2013
Typearticle
Languageen
FieldChemistry
TopicSynthesis and Biological Evaluation
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health ResearchNational Institute on Drug AbuseU.S. Public Health Service
KeywordsCYP2A6CotinineGenotypeMedicinePediatricsBiologyGeneticsNicotineInternal medicineGeneMetabolism

Abstract

fetched live from OpenAlex

The formation of cotinine, the main proximate metabolite and a biomarker of nicotine exposure, is mediated primarily by cytochrome P450 (CYP)2A6. Our aim was to determine whether higher cotinine levels in young children exposed to secondhand smoke (SHS) are a result of age-related differences in pharmacokinetics. Forty-nine participants, aged 2-84 months, received oral deuterium-labeled cotinine, with daily urine samples for up to 10 days for cotinine half-life measurement. DNA from saliva was used for CYP2A6 genotyping. The estimate of half-life using a mixed-effect model was 17.9 h (95% confidence interval: 16.5, 19.3), similar to that reported in adults. There was no statistically significant effect of sex, race, age, or weight. Children with normal-activity CYP2A6*1/*1 genotypes had a shorter half-life than those with one or two reduced-activity variant alleles. Our data suggest that higher cotinine levels in SHS-exposed young children as compared with adults are due to greater SHS exposure rather than to different cotinine pharmacokinetics.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.119
GPT teacher head0.401
Teacher spread0.282 · 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 designObservational
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
Published2013
Admission routes2
Has abstractyes

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