Anti-fibrotic effect of human amniotic fluid stem cells in biliary epithelial-mesenchymal transition of liver ductal organoid
Bibliographic record
Abstract
PURPOSE: In biliary atresia (BA), it has been demonstrated that biliary epithelial-mesenchymal transition (EMT) of reactive ductular cells is associated with liver fibrosis. This study aimed to develop an ex vivo biliary EMT model of liver ductal organoids for exploring the involvement of biliary EMT in fibrogenesis and to investigate whether human amniotic fluid stem cells (hAFSCs) can mitigate the biliary EMT process. METHODS: Liver ductal organoids were generated from the intrahepatic bile duct of healthy neonatal mice. Biliary EMT was induced in organoids by the administration of transforming growth factor beta-1 (TGF-β1) in culture medium. hAFSCs were co-cultured with organoids during biliary EMT induction. Expression of biliary epithelial cells, mesenchymal cells, myofibroblast, collagen I, and genes related to the Wnt signaling pathway were evaluated. RESULTS: Following administration of TGF-β1, we observed an increased expression of mesenchymal cell markers N-cadherin and Vimentin, as well as myofibroblast marker alpha-smooth muscle actin (α-SMA) in liver ductal organoids which were associated with increased expression of collagen 1. Administration of hAFSCs to organoids significantly attenuated TGF-β1-induced biliary EMT and collagen production. In addition, Wnt signaling was upregulated in biliary EMT, while hAFSCs downregulated the Wnt signaling resulting in decreased expression of myofibroblast and collagen in organoids. CONCLUSION: TGF-β1 is a potent cytokine that induces biliary EMT. hAFSCs significantly mitigated TGF-β1-induced biliary EMT in liver ductal organoids. The beneficial effect of hAFSCs administration is associated with the downregulation of the Wnt signaling pathway. This study indicates that hAFSCs can prevent the progression of liver fibrosis in BA.
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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 | 0.000 |
| 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".