Insulin secretion kinetics from single islets reveals distinct subpopulations
Bibliographic record
Abstract
Type II diabetes progresses with inadequate insulin secretion and prolonged elevated circulating glucose levels. Also, pancreatic islets isolated for transplantation or tissue engineering can be exposed to glucose over extended timeframe. We hypothesized that isolated pancreatic islets can secrete insulin over a prolonged period of time when incubated in glucose solution and that not all islets release insulin in unison. Insulin secretion kinetics was examined and modeled from single mouse islets in response to chronic glucose exposure (2.8-20 mM). Results with single islets were compared to those from pools of islets. Kinetic analysis of 58 single islets over 72 h in response to elevated glucose revealed distinct insulin secretion profiles: slow-, fast-, and constant-rate secretors, with slow-secretors being most prominent (ca., 50%). Variations in the temporal response to glucose therefore exist. During short-term (<4 h) exposure to elevated glucose few islets are responding with sustained insulin release. The model allowed studying the influence of islet size, revealing no clear effect. At high-glucose concentrations, when secretion is normalized to islet volume, the tendency is that smaller islets secrete more insulin. At high-glucose concentrations, insulin secretion from single islets is representative of islet populations, while under low-glucose conditions pooled islets did not behave as single ones. The characterization of insulin secretion over prolonged periods complements studies on insulin secretion performed over short timeframe. Further investigation of these differences in secretion profiles may resolve open-ended questions on pre-diabetic conditions and transplanted islets performance. This study deliberates the importance of size of islets in insulin secretion. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1059-1068, 2018.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".