Taking a Step Back: Insights into the Mechanisms Regulating Gut Epithelial Dedifferentiation
Bibliographic record
Abstract
Despite the environmental constraints imposed upon the intestinal epithelium, this tissue must perform essential functions such as nutrient absorption and hormonal regulation, while also acting as a critical barrier to the outside world. These functions depend on a variety of specialized cell types that are constantly renewed by a rapidly proliferating population of intestinal stem cells (ISCs) residing at the base of the crypts of Lieberkühn. The niche components and signals regulating crypt morphogenesis and maintenance of homeostatic ISCs have been intensely studied over the last decades. Increasingly, however, researchers are turning their attention to unraveling the mechanisms driving gut epithelial regeneration due to physical damage or infection. It is now well established that injury to the gut barrier triggers major cell fate changes, demonstrating the highly plastic nature of the gut epithelium. In particular, lineage tracing and transcriptional profiling experiments have uncovered several injury-induced stem-cell populations and molecular markers of the regenerative state. Despite the progress achieved in recent years, several questions remain unresolved, particularly regarding the mechanisms driving dedifferentiation of the gut epithelium. In this review, we summarize the latest studies, primarily from murine models, that define the regenerative processes governing the gut epithelium and discuss areas that will require more in-depth investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".