Activated Mucosal Mast Cells Differentiate Eosinophilic (Allergic) Esophagitis From Gastroesophageal Reflux Disease
Bibliographic record
Abstract
OBJECTIVES: To evaluate the utility of activated mucosal mast cells (MC) in the differential diagnosis of eosinophilic esophagitis (EE) and gastroesophageal reflux disease (GERD). METHODS: Intraepithelial eosinophils and MC were quantified in esophageal biopsies from 25 children with EE, 22 children with GERD and 22 controls. MCs were identified by immunohistochemistry for MC tryptase, whereas MC activation status was evaluated by immunohistochemistry for immunoglobulin E (IgE) and by electron microscopy. RESULTS: Esophageal biopsies from patients with EE showed higher intraepithelial eosinophil counts (55 +/- 27.5 vs 6.9 +/- 9.7, P < 0.0001) and MC counts (26.3 +/- 12.7 vs 7.8 +/- 8.9, P < 0.0001) than those from patients with GERD. Almost all EE biopsies (24 of 25 patients; 96%) contained IgE-bearing cells compared with 9 of 22 (41%) GERD biopsies (P < 0.001). GERD biopsies with intraepithelial eosinophil counts >7/high-power field (suggesting an allergic component) contained IgE-bearing cells in 6 of 7 (86%) cases compared to 3 of 15 (20%) cases with eosinophil counts <7/h.p.f (P < 0.01). No intraepithelial eosinophils, MC or IgE-positive cells were present in controls. Electron microscopy confirmed the presence of intraepithelial MC and changes in cytoplasmic granules indicative of MC and eosinophil activation. CONCLUSIONS: Intraepithelial MC counts and IgE-bearing cells may help to differentiate EE and GERD and to define a subset of GERD patients in which an allergic component is present. The findings support a role for a MC-mediated hypersensitivity reaction in the pathogenesis of EE.
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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 | 0.000 | 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".