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Record W4401190461 · doi:10.1177/10935266241265767

Interferon γ Expressing Mucosal Cells in Pediatric Chronic Inflammatory Bowel Disease

2024· article· en· W4401190461 on OpenAlexaff
Jefferson Terry

Bibliographic record

VenuePediatric and Developmental Pathology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsChildren's & Women's Health Centre of British Columbia
Fundersnot available
KeywordsInflammatory bowel diseaseMedicineInflammatory Bowel DiseasesInterferonInterferon γPathologyImmunologyInterferon gammaDiseaseCytokine

Abstract

fetched live from OpenAlex

The pathogenesis of Crohn's disease (CD) and ulcerative colitis (UC) is multifactorial and includes aberrations in the composition of gastrointestinal mucosal inflammatory cells. Accurate identification of CD and UC is important as treatment and prognosis differs; however, CD and UC may be difficult to differentiate. Interferon γ (IFNγ) expression appears to be increased in ileal mucosa from CD patients, implying that IFNγ could be a diagnostically useful marker to differentiate CD from UC. This study uses automated assessment of IFNγ immunohistochemical expression in archival GI mucosal biopsies from stomach, duodenum, terminal ileum, and colon in a pediatric population to address this possibility. IFNγ positive mucosal cells are increased in the colon in both CD and UC compared to normal colon and in the ileum of CD compared to normal and UC. The abundance of IFNγ positive cells is not correlated with the presence of active inflammation, indicating that active inflammation is not responsible for the variance in abundance of IFNγ positive cells between cohorts and sites. Overlap between CD, UC, and normal suggests that IFNγ immunohistochemistry may only be clinically useful in select situations such as undetermined inflammatory bowel disease and additional study in these areas is warranted.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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