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Fas Polymorphisms Influence Susceptibility to Autoimmune Hepatitis

2005· article· en· W2065396078 on OpenAlexfundno aff
Akira Hiraide, Fumio Imazeki, Osamu Yokosuka, Tatsuo Kanda, Hiroshige Kojima, Kenichi Fukai, Yoichi Suzuki, Akira Hata, Hiromitsu Saisho

Bibliographic record

VenueThe American Journal of Gastroenterology · 2005
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsnot available
FundersAlberta Innovates - Health Solutions
KeywordsAutoimmune hepatitisHaplotypeMedicinePrimary biliary cirrhosisAlleleImmunologyFas receptorSingle-nucleotide polymorphismGenetic predispositionAllele frequencyGeneHepatitisGenotypeInternal medicineGeneticsBiologyDiseaseApoptosis

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Genetic factors associated with autoimmune hepatitis (AIH) and primary biliary cirrhosis (PBC), immune-mediated chronic inflammatory liver diseases of unknown etiology, remain to be elucidated. Polymorphisms of the gene encoding Fas have been linked to a variety of autoimmune diseases. We hypothesized that Fas gene polymorphisms might be genetic markers for AIH and PBC. METHODS: To determine the frequency and significance of Fas polymorphisms in patients with AIH and PBC, 74 Japanese AIH patients, 98 Japanese PBC patients, and 132 ethnically matched control subjects were investigated by the use of the Taqman assay. RESULTS: We found significant differences between AIH patients and controls in allele frequencies of Fas-670 (p=0.009), Fas IVS (intervening sequence) 2nt176 (p=0.018), Fas IVS3nt46 (p=0.031), and Fas IVS5nt82 (p=0.013) polymorphisms. Haplotype analysis revealed that one of the haplotypes, GATGC, was associated with increased AIH prevalence. On the other hand, we found no statistically significant differences between PBC patients and controls in allele frequencies of the Fas polymorphisms genotyped in this study. CONCLUSIONS: These results indicate a genetic link of Fas polymorphisms to the development of AIH. Further studies are needed to determine the genetic factors contributing to the development of AIH.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.015
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.008
GPT teacher head0.258
Teacher spread0.251 · 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.

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

Citations73
Published2005
Admission routes1
Has abstractyes

Explore more

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