Evaluating Therapeutic Interventions in the SHIP-deficient Mouse Model of Crohn Disease-like Ileitis and Fibrosis
Bibliographic record
Abstract
Crohn disease (CD) is a chronic, relapsing inflammatory condition characterized by segmental, transmural inflammation that can affect any part of the gastrointestinal tract. A hallmark of CD is impaired intestinal barrier function, and intestinal fibrosis is a common complication. SHIP-deficient (SHIP-/-) mice spontaneously develop gut barrier dysfunction, ileal inflammation, and fibrotic pathology, recapitulating key features of CD-like ileitis. The SHIP-/- mice serve as a valuable model for testing anti-inflammatory and anti-fibrotic therapies in CD. Here, we describe methods to evaluate therapeutic interventions in this model, using dexamethasone as an example of an effective therapy. We provide a step-by-step protocol for characterizing disease and treatment response, including in vivo assessment of gut permeability measured by FITC-dextran, as well as gross pathological examination and detailed histological analyses. Histological assessments incorporate hematoxylin and eosin (H&E) staining for inflammation, Masson's trichrome staining for fibrosis, and Alcian blue/Periodic Acid-Schiff (PAS) staining for goblet cell hyperplasia and hypertrophy. Additionally, cytokines and inflammatory markers in ileal tissue are quantified to assess the inflammatory state. Together, these methods provide a comprehensive framework for evaluating treatment efficacy in the SHIP-/- mouse model of CD-like ileitis. This protocol is broadly applicable to investigators studying gut inflammation, mucosal healing, and intestinal fibrosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".