Ca<sup>2+</sup> controls slow NAD(P)H oscillations in glucose‐stimulated mouse pancreatic islets
Bibliographic record
Abstract
Exposure of pancreatic islets of Langerhans to physiological concentrations of glucose leads to secretion of insulin in an oscillatory pattern. The oscillations in insulin secretion are associated with oscillations in cytosolic Ca(2+) concentration ([Ca(2+)](c)). Evidence suggests that the oscillations in [Ca(2+)](c) and secretion are driven by oscillations in metabolism, but it is unclear whether metabolic oscillations are intrinsic to metabolism or require Ca(2+) feedback. To address this question we explored the interaction of Ca(2+) concentration and islet metabolism using simultaneous recordings of NAD(P)H autofluorescence and [Ca(2+)](c), in parallel with measurements of mitochondrial membrane potential (DeltaPsi(m)). All three parameters responded to 10 mm glucose with multiphasic dynamics culminating in slow oscillations with a period of approximately 5 min. This was observed in approximately 90% of islets examined from various mouse strains. NAD(P)H oscillations preceded those of [Ca(2+)](c), but their upstroke was often accelerated during the increase in [Ca(2+)](c), and Ca(2+) influx was a prerequisite for their generation. Prolonged elevations of [Ca(2+)](c) augmented NAD(P)H autofluorescence of islets in the presence of 3 mm glucose, but often lowered NAD(P)H autofluorescence of islets exposed to 10 mm glucose. Comparable rises in [Ca(2+)](c) depolarized DeltaPsi(m). The NAD(P)H lowering effect of an elevation of [Ca(2+)](c) was reversed during inhibition of mitochondrial electron transport. These findings reveal the existence of slow oscillations in NAD(P)H autofluorescence in intact pancreatic islets, and suggest that they are shaped by Ca(2+) concentration in a dynamic balance between activation of NADH-generating mitochondrial dehydrogenases and a Ca(2+)-induced decrease in NADH. We propose that a component of the latter reflects mitochondrial depolarization by Ca(2+), which reduces respiratory control and consequently accelerates oxidation of NADH.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".