Proteomic analysis of ascending colon biopsies from a paediatric inflammatory bowel disease inception cohort identifies protein biomarkers that differentiate Crohn's disease from UC
Bibliographic record
Abstract
OBJECTIVE: Accurate differentiation between Crohn's disease (CD) and UC is important to ensure early and appropriate therapeutic intervention. We sought to identify proteins that enable differentiation between CD and UC in children with new onset IBD. DESIGN: Mucosal biopsies were obtained from children undergoing baseline diagnostic endoscopy prior to therapeutic interventions. Using a super-stable isotope labeling with amino acids in cell culture (SILAC)-based approach, the proteomes of 99 paediatric control and biopsies of patients with CD and UC were compared. Multivariate analysis of a subset of these (n=50) was applied to identify novel biomarkers, which were validated in a second subset (n=49). RESULTS: In the discovery cohort, a panel of five proteins was sufficient to distinguish control from IBD-affected tissue biopsies with an AUC of 1.0 (95% CI 0.99 to 1.0); a second panel of 12 proteins segregated inflamed CD from UC within an AUC of 0.95 (95% CI 0.86 to 1.0). Application of the two panels to the validation cohort resulted in accurate classification of 95.9% (IBD from control) and 80% (CD from UC) of patients. 116 proteins were identified to have correlation with the severity of disease, four of which were components of the two panels, including visfatin and metallothionein-2. CONCLUSIONS: This study has identified two panels of candidate biomarkers for the diagnosis of IBD and the differentiation of IBD subtypes to guide appropriate therapeutic interventions in paediatric patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".