P131 Serum Proteomics and Intestinal Ultrasound Differentiates Fibrostenotic and Inflammatory Crohn’s Disease
Bibliographic record
Abstract
Abstract Background Fibrostenotic Crohn’s disease (CD) is a challenging phenotype often leading to surgical resection. Easily accessible and cost-effective diagnostic tools are needed to advance precision medicine to manage fibrostenotic CD patients. To date, there are no biomarkers that can discriminate stricturing CD from other phenotypes. Early studies suggest that protein biomarkers identified from serum proteomics may differentiate CD subtypes. Utilizing intestinal ultrasound (IUS), which readily detects strictures, the serum of patients with and without strictures was collected for proteomic characterization. Methods All consecutive CD patients attending outpatient appointments received IUS, and CT (computed tomography)/MR (magnetic resonance) within 6 months of study inclusion. Strictures were defined as a fixed segment of the ileum with increased bowel wall thickness (BWT) and luminal apposition with or without prestenotic dilation. Thirty two patients with ileal strictures were matched with CD patients without strictures (inflammatory behaviour). Serum of patients were collected for quantitative shotgun proteomics using liquid chromatography and tandem-mass spectrometry (LC-MS/MS). Results 64 patients in the stricture group had a significantly greater mean ileal bowel wall thickness (7.5 mm) compared to the inflammatory group (4.5mm, p = 0.02). A distinct and statistically significant protein signature was discovered between both patient populations. In the stricture group: plexin-A2, CD5 antigen-like protein, neogenin and dystonin were found, while in the non-strictured patients, matrix metalloproteinase 16, C-reactive protein, vinculin and apolipoprotein C-III were detected. Using Metascape and STRING-db, gene ontology and reactome pathway analyses, we identified enrichment of B cell differentiation and muscle contraction in the stricture patients, whereas in the non-stricture group, an enrichment for high-density lipoprotein remodeling, innate immune system and calcium ion transport were found. Conclusion We identified a unique protein signature that could robustly distinguish CD strictures from inflammatory phenotypes. This innovative diagnostic preliminary protein panel will be further expanded and validated in inflammatory and fibrostenotic CD populations, potentially allowing for future clinical decision optimization.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".