Editorial: Islet cell development, heterogeneity and regeneration
Bibliographic record
Abstract
towards SC-islets, which can aid in optimizing protocols for producing safe and functional SC-β cells. The other review article by Doherty et al. discusses the critical role of the native islet microenvironment, which undergoes disruption during the isolation process, eliciting tissue injury responses. It emphasizes the importance of reconstructing the islet niche by integrating non-endocrine components, such as vasculature, and extracellular matrix elements like the interstitial matrix and basement membrane. This approach holds promise not only for improving the success rate of human islet engraftment but also for advancing the bioengineering of SC-islets. Different strategies have been undertaken to enhance the function of the dysfunctional β-cells (7). For instance, interest in plants and phytochemicals for managing blood glucose levels has surged, but the mechanisms behind their effectiveness are largely unexplored (8). In the work authored by Paul et al., the cellular mode of action of pheophorbide A, a derivative of chlorophyll, was investigated. They found that pheophorbide A influences the efficiency of glucose transporters (GLUTs), thus enhancing glucose uptake. The study proposes the GLUT1-Pheophorbide A complex as a promising therapeutic option for improving blood glucose levels in diabetes. In another study, Mahmoudi-Aznaveh et al., explored a novel perspective on liver-pancreas interplay in insulin-resistant conditions. They found that liver-derived exosomes enhance the expression of key β-cell markers such as insulin. This indicates that specific molecular cargoes within exosomes can regulate glucose homeostasis by enhancing β-cell functionality. Their findings emphasize the potential of exosomes as delivery systems for diabetes therapy, underscoring the need for further investigation into their molecular contents and impact on the functional response of β-cells in obesity and diabetes. Advancing our understanding of β-cell biology requires the development of novel tools to probe into structure and function of islets. For instance, mathematical models can assist in elucidating the intricate interplay between metabolic signaling pathways and glucose homeostasis (9). Along this line, Mahesvare et al., employed the Systems Biology Markup Language (SBML) to construct a comprehensive kinetic model of glucose-stimulated insulin secretion (GSIS). By applying this model to both clinical and experimental datasets across different species, they were able to elucidate the associated alterations in glycolysis within pancreatic β-cells. In another advancement, Pfeifer et al. designed an open-source tool called "PyCreas" for fast and accurate quantitative analysis of islet architecture. To assess the efficacy of the PyCreas tool in quantifying cell distribution within islets across various metabolic states, they investigated the localization and distribution patterns of endocrine cells during gestation and prediabetes states in mouse models. This novel tool will allow a systematic quantitative approach for analyzing islet cell distribution that can aid in examining the pathophysiology of metabolic disorders. The studies presented in this research topic enhance our understanding of β-cell biology, offering new avenues to explore islet structure, function, and β-cell regeneration. Moving forward, it is imperative to direct our focus toward leveraging the complexities of human β-cell development and physiology for improved cell therapy, and to explore diverse approaches for restoring β-cell mass or function in vivo. Additionally, understanding how different regenerative strategies can be tailored to suit the diverse needs of patients with diabetes complications is crucial and warrants further exploration. We commend the authors for their invaluable contributions to this special issue on islet biology.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.027 | 0.017 |
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".