Functional Plasticity of the Human Infant β-Cell Exocytotic Phenotype
Bibliographic record
Abstract
Our understanding of adult human β-cells is advancing, but we know little about the function and plasticity of β-cells from infants. We therefore characterized islets and single islet cells from human infants after isolation and culture. Although islet morphology in pancreas biopsies was similar to that in adults, infant islets after isolation and 24-48 hours of culture had less insulin staining, content, and secretion. The cultured infant islets expressed pancreatic and duodenal homeobox 1 and several (Glut1, Cav1.3, Kir6.2) but not all (syntaxin 1A and synaptosomal-associated protein 25) markers of functional islets, suggesting a loss of secretory phenotype in culture. The activity of key ion channels was maintained in isolated infant β-cells, whereas exocytosis was much lower than in adults. We examined whether a functional exocytotic phenotype could be reestablished under conditions thought to promote β-cell differentiation. After a 24- to 28-day expansion and maturation protocol, we found preservation of endocrine markers and hormone expression, an increased proportion of insulin-positive cells, elevated expression of syntaxin 1A and synaptosomal-associated protein 25, and restoration of exocytosis to levels comparable with that in adult β-cells. Thus, human infant islets are prone to loss of their exocytotic phenotype in culture but amenable to experimental approaches aimed at promoting expansion and functional maturation. Control of exocytotic protein expression may be an important mechanism underlying the plasticity of the secretory machinery, an increased understanding of which may lead to improved regenerative approaches to treat diabetes.
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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) | 0.002 | 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".