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Record W2801891796 · doi:10.1111/hepr.13188

Marked accumulation of fluorodeoxyglucose and inflammatory cells expressing glucose transporter‐3 in immunoglobulin G4‐related autoimmune hepatitis

2018· article· en· W2801891796 on OpenAlexfundno aff
Toshihiro Araki, Teruko Arinaga‐Hino, Hironori Koga, Jun Akiba, Tatsuya Ide, Yoshinobu Okabe, Reiichiro Kuwahara, Keisuke Amano, Makiko Yasumoto, Toshihiro Kawaguchi, Tomoya Sano, Reiichiro Kondou, Seiji Kurata, Keiichi Mitsuyama, Takuji Torimura

Bibliographic record

VenueHepatology Research · 2018
Typearticle
Languageen
FieldMedicine
TopicIgG4-Related and Inflammatory Diseases
Canadian institutionsnot available
FundersAlberta Innovates - Health Solutions
KeywordsAutoimmune hepatitisMedicinePathogenesisPathologyAntibodyLiver biopsyIgG4-related diseaseLymphBiopsyImmunologyDisease

Abstract

fetched live from OpenAlex

Immunoglobulin (Ig)G4‐related autoimmune hepatitis (AIH) is a recently proposed subtype that responds well to steroid treatment; however, its pathogenesis remains unclear. We report here a 65‐year‐old Japanese woman with skin itching and lip swelling. She had liver injury with jaundice, which persisted despite stopping anti‐allergic agents. Blood chemistry revealed highly elevated serum IgG and IgG4 (535 mg/dL) levels, and positive anti‐nuclear antibody. The diagnosis of AIH was based on liver biopsy. Notably, the IgG4 + /IgG + cell ratio was 85%. On fluorodeoxyglucose (FDG) positron emission tomography/computed tomography, robust signal intensity was found in the liver, and in enlarged lymph nodes and salivary glands with confirmed IgG4 + cell infiltration. Immunofluorescence analysis of the liver biopsy specimen indicated clear expression of glucose transporter‐3 (Glut‐3) in IgG4 + inflammatory cells infiltrating into the portal area. This is the first report of simultaneous strong accumulation of FDG and Glut‐3 expression in IgG4‐related AIH, which might aid in elucidating the pathogenesis of this disease.

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)0.001
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 score0.239
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.366
Teacher spread0.310 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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