Friend and foe: β-cell Ca2+ signaling and the development of diabetes
Bibliographic record
Abstract
The divalent cation Calcium (Ca2+) regulates a wide range of processes in disparate cell types. Within insulin-producing β-cells, increases in cytosolic Ca2+ directly stimulate insulin vesicle exocytosis, but also initiate multiple signaling pathways. Mediated through activation of downstream kinases and transcription factors, Ca2+-regulated signaling pathways leverage substantial influence on a number of critical cellular processes within the β-cell. Additionally, there is evidence that prolonged activation of these same pathways is detrimental to β-cell health and may contribute to Type 2 Diabetes pathogenesis. This review aims to briefly highlight canonical Ca2+ signaling pathways in β-cells and how β-cells regulate the movement of Ca2+ across numerous organelles and microdomains. As a main focus, this review synthesizes experimental data from in vitro and in vivo models on both the beneficial and detrimental effects of Ca2+ signaling pathways for β-cell function and health. Acute increases in intracellular Ca2+ stimulate a number of signaling cascades, resulting in (de-)phosphorylation events and activation of downstream transcription factors. The short-term stimulation of these Ca2+ signaling pathways promotes numerous cellular processes critical to β-cell function, including increased viability, replication, and insulin production and secretion. Conversely, chronic stimulation of Ca2+ signaling pathways increases β-cell ER stress and results in the loss of β-cell differentiation status. Together, decades of study demonstrate that Ca2+ movement is tightly regulated within the β-cell, which is at least partially due to its dual roles as a potent signaling molecule.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".