Maintenance of β-Cell Maturity and Plasticity in the Adult Pancreas
Bibliographic record
Abstract
Diabetes occurs when the pancreas fails to produce and secrete sufficient insulin for the maintenance of glucose homeostasis. Although many factors required for β-cell development have been elucidated, we know surprisingly little regarding the mechanisms that maintain the differentiated state of adult β-cells. Despite years of fruitful research, many hurdles remain before we can replace failing β-cells in patients. Efforts to produce new β-cells will benefit from detailed knowledge of their differentiation, maturation, maintenance, heterogeneity, and plasticity. Studies of single cells and conditional knockout mice reveal surprising relationships between the different islet cell types and previously unappreciated roles for transcription factors and soluble factors in β-cell maintenance. Herein we highlight numerous innovative efforts made to identify the core mechanisms responsible for β-cell phenotypic maintenance and compare these with other long-lived cell types. In the adult pancreas, physical β-cell mass is determined by the balance of β-cell birth, differentiation, size, and death. The prenatal origins and development of β-cells (1,2), the molecular mechanisms of adult β-cell proliferation (3,4), and programmed β-cell death (5) have all been reviewed recently and will not be revisited here. The maintenance and adaptation of β-cell mass after birth involves the addition of new β-cells (6). Cells with robust staining for insulin have been shown to slowly proliferate in vivo and in vitro (7–9). It has been reported that β-cells have an extremely long life span under typical conditions and proliferation plays a diminishing role with advancing age (7,10,11). The apparent low rate of β-cell replication has led some groups to suggest that adult β-cells arise from a pool of non–β-cell progenitors (12). Because this topic has been hotly debated and reviewed recently (13), we will only briefly discuss studies relevant to other aspects of this review. …
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".