Exploration of individual beta cell function over time <i>in vivo:</i> effects of hyperglycemia and glucagon-like peptide-1 receptor (GLP1R) agonism
Bibliographic record
Abstract
Abstract The coordinated function of beta cells within the pancreatic islet is required for the normal regulation of insulin secretion and is partly controlled by specialized “leader” and highly connected “hub” beta-cell subpopulations. Whether cells within these subpopulations are functionally stable in vivo remains unclear. Here, we establish an approach to monitor Ca 2+ dynamics within individual beta cells over time, after engraftment into the anterior eye chamber, where continuous blood perfusion and near normal innervation pertain. Under normoglycemic conditions, islet network dynamics, and the behavior of individual leaders and hubs, remain stable for at least seven days. Hyperglycemia, resulting from high-fat diet feeding or the loss of a host Gck allele, caused engrafted islets to display incomplete and abortive Ca 2+ waves and overall connectivity was diminished. Whereas hub cell numbers were lowered profoundly in both disease models, leaders largely persisted. Treatment with the GLP1R agonist Exendin-4 led to a recovery of islet-wide Ca 2+ dynamics and the re-emergence of hub cells within minutes, with the effects of the incretin mimetic being more marked than those observed after analogous treatments in vitro . Similar observations were made using 3-dimensional imaging across the whole islet. Our findings thus suggest that incretins may act both directly and indirectly on beta cells in vivo. The approach described may provide broad applicability to the exploration of individual cell function over time in the living animal.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".