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Intercellular adhesion molecule‐1 gene polymorphisms in Behçet’s disease

2000· article· en· W1987790507 on OpenAlexaff
David H. Verity, R. W. Vaughan, E. Kondeatis, W. Madanat, Harran Y. Zureikat, F. Fayyad, Jane Marr, Charlie Kanawati, Graham R. Wallace, Miles Stanford

Bibliographic record

VenueEuropean Journal of Immunogenetics · 2000
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsBiologyAlleleBehcet's diseaseGeneDiseaseInflammatory bowel diseaseMajor histocompatibility complexImmunologyGeneticsIntercellular Adhesion Molecule-1ICAM-1Cell adhesion moleculeMedicinePathology

Abstract

fetched live from OpenAlex

Intercellular adhesion molecule-1 (ICAM-1) gene polymorphisms have been implicated in the susceptibility to inflammatory diseases, including multiple sclerosis and inflammatory bowel disease. The expression of both soluble and tissue ICAM-1 is increased in Behçet's disease (BD) but the contribution of ICAM-1 gene polymorphisms to this disease remains unknown. Associations with BD have been reported for genes within the MHC, including HLA-B51, TNF and MICA, but the role of non-MHC genes in BD remains largely unexplored. We have investigated the frequency of the R/G 241 and K/E 469 ICAM-1 gene polymorphisms in 83 patients with BD disease and 103 healthy controls, all of Palestinian and Jordanian descent, and demonstrated an association between BD and the ICAM-1 E469 allele (Pc = 0.046, OR = 2.1). Among patients, no association was found between the presence of ocular disease and ICAM-1 polymorphisms. While the functional correlate of this polymorphism remains unclear, this finding indicates that a genetic polymorphism in the ICAM-1 gene domain, which is independent of the MHC, may contribute to 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 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.257
Teacher spread0.238 · 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

Citations105
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of ImmunogeneticsSame topicSystemic Lupus Erythematosus ResearchFrench-language works237,207