Identification of immune cell markers associated with ulcerative colitis histological disease activity in colonic biopsies
Bibliographic record
Abstract
AIMS: Accurate determination of histological activity in ulcerative colitis (UC) is essential given its diagnostic and prognostic importance. Data on the relationship between histology and immune cell markers are limited. We aimed to evaluate the association between histological disease activity and immune cell marker concentration in colonic biopsies from patients with UC. METHODS: Sigmoid colon biopsies from 20 patients with UC were retrospectively assessed using the Robarts Histopathology Index (RHI). Targeted mass spectrometry determined the concentration of 18 immune cell markers (cluster of differentiation (CD) 4, CD8, CD19, CD20, CD40, CD56, CD68, CD103, forkhead box p3 (FOXP3), human leucocyte antigen, DR alpha chain (HLA-DRA), interleukin 10 (IL-10), IL-23 subunit alpha (IL-23A), IL-23 receptor (IL-23R), IL-2 receptor alpha chain (IL-2RA), Ki67, lymphocyte-activation gene 3 (LAG-3), programmed cell death protein 1 (PD-1) and PD ligand 1 (PD-L1)). The association between RHI score and immune cell marker concentration was quantified using Spearman's rank correlation coefficient (ρ) and related 95% CIs. RESULTS: Fourteen of the 18 immune cell marker proteins were detected, with tissue concentration ranging from 0.003 to 11.53 fmol/µg. The overall RHI score was positively correlated with CD19, CD20, CD40, FOXP3, LAG-3, PD-1 and PD-L1 concentration (ρ=0.596-0.799) and negatively correlated with CD56 concentration (ρ=-0.460). There was no significant association between RHI score and CD4, CD8, CD68, CD103, HLA-DRA or Ki67 concentration. CONCLUSIONS: This study provides insight into the correlation between immune cell marker expression and histological disease activity and the possible molecular and immunological determinants underlying microscopic disease activity in UC.
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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 | 0.002 | 0.002 |
| 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".