Liver Organoids Generated from Mice with Necrotizing Enterocolitis Have Reduced Regenerative Capacity
Bibliographic record
Abstract
INTRODUCTION: Necrotizing enterocolitis (NEC) is one of the most severe gastrointestinal diseases in infancy. NEC can cause metabolic derangements, multi-organ injury including severe liver damage. The mechanism leading to hepatic damage in NEC remains unclear. The aim of this study is to establish and characterize liver organoids from NEC mice. MATERIALS AND METHODS: Following ethical approval (#44032), we induced experimental NEC from postnatal day 5 (P5) to P9 using C57BL/6 mice pups. NEC was induced by gavage formula feeding, gavage lipopolysaccharide (LPS) administration, and hypoxia. Breastfed pups were used as control. On P9, NEC and control pups were sacrificed and liver tissue was harvested and organoids were generated. Organoid size was recorded daily (day 2-4) by measuring their surface area and organoid growth was calculated. RNA was extracted on day 4 after liver organoid generation. RESULTS: Organoid growth rate was significantly lower in NEC liver organoids compared to control liver organoids. mRNA expression of liver progenitor cells markers of LGR5 and SOX9 was lower in NEC liver organoids compared to control liver organoids. Similarly, expression of proliferation markers of Ki67 and PCNA was lower in NEC liver organoids. CONCLUSION: We report a novel technique to generate liver organoids during NEC. These organoids are characterized by reduced progenitor cells, reduced proliferation, and overall impaired regenerative capacity. Liver progenitor cells are important targets to prevent liver damage in NEC and promote recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".