Profiles of Lamina Propria T Helper Cell Subsets Discriminate Between Ulcerative Colitis and Crohnʼs Disease
Bibliographic record
Abstract
BACKGROUND: Distinction between 2 forms of inflammatory bowel disease (IBD), ulcerative colitis (UC) and Crohn's disease (CD), can be challenging. Aberrant mucosal immunity suggests that CD is a T helper type 1 cell (Th1)-driven disease, whereas UC as Th2-driven response. However, whether this paradigm truly distinguishes CD from UC is controversial. We aimed to clarify the discriminating potential of lamina propria Th subsets in patients with IBD. METHODS: Biopsies from 79 patients with IBD and 20 healthy controls were collected for Th subsets analysis (Th1:interferon γ [IFN-γ], T-bet; Th2:interleukin 13 [IL-13], Gata3; Th17:IL-17, RORγt; Treg:FoxP3). The receiver-operating characteristic curves were constructed to assess the discriminating ability by calculating the area under the receiver-operating characteristic curve. The equation with the highest area under the receiver-operating characteristic curve was applied to newly diagnosed patients to evaluate discriminating ability. RESULTS: Patients with CD showed increased IFN-γ or T-bet cells and decreased IL-13 or Gata3 cells compared with UC. A discriminant equation composed of 4 markers (IFN-γ, T-bet, IL-13, and Gata3) yielded the highest area under the receiver-operating characteristic curve. In 36 established CD or UC, the sensitivity, specificity, positive and negative predictive probabilities were 92.6%, 55.6%, 86.2%, and 71.4% and in 14 newly diagnosed patients were 100.0%, 42.9%, 63.6%, and 100.0%. Furthermore, Gata3 cells were increased in tumor necrosis factor inhibitor therapy nonresponders compared with responders in CD. IFN-γ cells were directly and inversely proportional to disease activity in patients with CD and UC, respectively. CONCLUSIONS: The Th1/Th2 paradigm can distinguish CD from UC and may be further associated with response to tumor necrosis factor inhibitor in CD and disease activity in patients with IBD.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".