INFLIXIMAB THERAPY RESTORES NORMAL GROWTH IN CHILDREN WITH CHRONICALLY ACTIVE SEVERE CROHN DISEASE REFRACTORY TO IMMUNOMODULATORY THERAPY
Bibliographic record
Abstract
Introduction: Coeliac disease (CD) is a complex genetic disorder.Besides the environmental factor gluten and the HLA-DQ2 and 8 proteins, other unknown genetic factors are involved.Several genome-wide screens have been performed to locate the regions with genes involved in CD.In the Dutch population this has led to the discovery of two susceptibility regions, 6q21-22 and 19p13 (CELIAC4).The region on 19p13 is only limited to 3.5 Mb, but since it is a region with a high density of genes, it still contains 92 candidate genes.Aim: We set out to fine-map this region with microsatellite markers and single nucleotide polymorphisms (SNPs) to search for association between genes and CD.Methods: We started with a cohort of 216 cases and 216 controls and expanded this to 311 cases and 540 controls.Microsatellites and SNPs were used.Results: Association testing using microsatellite markers has revealed a small region of interest of around 450 kb.Further fine-mapping with SNP shows association in a 150 kb region, encompassing a limited number of genes.Adding more SNPs led to the discovery of MYO9B as the gene on 19p13 most strongly associated to CD.This gene, which is a singleheaded motor myosin, shows association in its 3' part.This part of the gene contains the most interesting domains, which also differentiate the role of this myosin from the other family members.The Rho-Gap and Dag-Pe domains indicate that this gene is involved in signal transduction.We are now elucidating the specific role of this gene in signal transduction and trying to incorporate it in our models for CD.Conclusion: Finemapping the Dutch CD linkage region on chromosome 19p13 has led us to the gene MYO9b which is associated with the disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".