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Record W2115107413 · doi:10.1081/ncn-200027641

Allele Frequency of Inosine Triphosphate Pyrophosphatase Gene Polymorphisms in a Japanese Population

2004· article· en· W2115107413 on OpenAlexaff
A. Marinaki, Satoshi Sumi, M Arenas, Lynette D. Fairbanks, Shinji Harihara, Kazuhiro Shimizu, A. Ueta, J. A. Duley

Bibliographic record

VenueNucleosides Nucleotides & Nucleic Acids · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsITPAAlleleAllele frequencyThiopurine methyltransferaseBiologyPolymorphism (computer science)Inosine monophosphateGeneticsGeneGenotypeAzathioprineMedicineNucleotideInternal medicine

Abstract

fetched live from OpenAlex

The enzyme inosine triphosphate pyrophosphatase (ITPase) catalyses the pyrophosphohydrolysis of ITP to IMP. ITPase deficiency is a clinically benign autosomal recessive condition characterised by the abnormal accumulation of ITP in erythrocytes. A deficiency of ITPase may predict adverse reactions to therapy with the thiopurine drug 6-mercaptopurine and its prodrug azathioprine. In this study, we examine the frequencies of ITPA polymorphisms in 100 healthy Japanese individuals. The allele frequency of the 94C > A variant in the Japanese sample was 0.135 (Caucasian allele frequency 0.06). The IV2 + 21A > C polymorphism was not found in Japanese (Caucasian allele frequency 0.130). Allele frequencies of the 138G > A, 561G > A and 708G > A polymorphisms were 0.57, 0.18 and 0.06 respectively in the Japanese population, and with the exception of the 138G > A polymorphism, similar to allele frequencies in Caucasians.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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

Citations20
Published2004
Admission routes1
Has abstractyes

Explore more

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