Pancreatic islet organoids and organoids on-chip for type 1 diabetes
Bibliographic record
Abstract
Type 1 Diabetes (T1D) is a highly complex and prevalent metabolic disease caused by dysfunctions of pancreatic islets. Over the past decade, diabetes research and treatments have focused on insulin restoration and glucose homeostasis, especially the regenerative approaches for stem cell based therapies for T1D. Nevertheless, unravelling the islet developmental processes and physiopathology of diabetes requires development of in vitro models that mimic the structure and function of islet of Langerhans. Organoids have progressively revolutionized three-dimensional cell culture allowing development of more physiologically relevant models that recapitulate cellular interactions and responses more accurately. Here, we provide insights into advanced islet organoid models focusing on their generation, characteristics, applications, and challenges. We discuss state-of-the-art tissue engineering strategies to recapitulate islet development and pancreatic niche microenvironment by exploring different cell sources of insulin-producing cells, including primary islet cells and cell line aggregation, transdifferentiation from adult somatic cells, and differentiation from stem cells. We discuss the significance of replicating the islet microenvironment through extracellular matrices and scaffolds, as well as vascular and immunomodulatory approaches. We highlight the potential of organ-on-chip technologies to closely recapitulate the complex microenvironment of pancreatic tissues providing platforms for disease modelling, drug screening and regenerative medicine. Despite the challenges, islet organoids combined with microfluidics represent a promising tool for the understanding of T1D pathogenesis and developing innovative therapies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".