ReviewJournal of translational medicine2026
Pancreatic organoids and organoid-on-a-chip platforms: from disease modeling to precision therapy.
Review in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
10 authors.
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Abstract
backgroundPancreatic diseases, including diabetes, pancreatic ductal adenocarcinoma, pancreatitis, and cystic fibrosis, impose a substantial clinical burden and are challenging to model using conventional experimental systems. Pancreatic organoids provide physiologically relevant three-dimensional models that recapitulate the key structural and functional features of the human pancreas. This review summarizes recent advances in pancreatic organoid and organoid-on-a-chip technologies, focusing on their applications in pancreatic disease modeling, therapeutic screening, and potential relevance to personalized treatment. MAIN TEXT: This review discusses the construction of pancreatic organoids and pancreatic organoid-on-a-chip platforms, including key culture parameters and practical strategies for model establishment. The applications of pancreatic organoids in modeling pancreatic cancer, diabetes, pancreatitis, and cystic fibrosis are reviewed. In addition, the role of patient-derived organoids in therapeutic screening and their potential integration into personalized treatment workflows are evaluated. The enabling role of artificial intelligence in pancreatic organoid research is also discussed, followed by an overview of the current challenges and future perspectives.
conclusionPancreatic organoid-based platforms are promising tools for pancreatic disease modeling and therapeutic evaluation. Addressing the current limitations of cellular complexity, vascularization, reproducibility, and scalability is essential for expanding their research and clinical utility.
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