Impairment of stromal-epithelial regenerative cross-talk in Hirschsprung disease primes for the progression to enterocolitis
Bibliographic record
Abstract
Hirschsprung disease (HSCR) is a congenital condition characterized by the improper migration of enteric neural crest cells, leading to aganglionosis most commonly in the rectosigmoid colon. This severe and life-threatening disorder often results in the development of Hirschsprung-associated enterocolitis (HAEC), which can occur either before or after surgical resection of the affected bowel segment. Using colonic tissue from patients with HSCR alongside the well-established endothelin receptor B knockout mouse model, we investigated epithelial regeneration dynamics and stromal-epithelial cross-talk in the distal ganglionic colon, a critical site for HAEC development. In individuals with HSCR but without epithelial damage, the distal ganglionic colon displayed impaired epithelial regeneration and alteration of intestinal stem cell dynamics, characterized by the reduction of leucine-rich repeat-containing G protein–coupled receptor 5 (LGR5 + ) epithelial stem cells. This phenomenon was consistent in the mouse model, where impaired regenerative ability preceded HAEC when epithelial damage occurred on site. Patients with HSCR also exhibited remodeling in stromal cells in this distal ganglionic colon region, with fewer primary sources of Wingless-related integration site (Wnt) signal-releasing stromal cells and the exclusive presence of proinflammatory (matrix metalloproteinase 1 + ) stromal cells. Stromal cells from the HSCR distal ganglionic colon failed to sustain the growth of colonic organoids. However, ibuprofen suppressed the proinflammatory stromal cells, leading to effective restoration of epithelial organoid growth. These observations underscore the crucial role of impaired stromal-epithelial cross-talk in HSCR and the pathogenesis of HAEC and suggest potential therapeutic targets for the prevention or treatment of the condition.
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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 | 0.000 |
| 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 | 0.001 |
| 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".