Paracrine GABA and insulin restrain pancreatic α‐cell proliferation in islets of Langerhans
Bibliographic record
Abstract
In type 1 diabetes loss of pancreatic β‐cells is followed by a rapid increase of α‐cells. However, the underlying mechanisms are not fully understood. β‐cells express glutamic acid decarboxylase (GAD) and produce a large amount of γ‐aminobutyric acid (GABA), which, together with insulin, inhibits α‐cell secretion of glucagon. We investigated the role of GABA and insulin in the regulation of α‐cell proliferation in vitro and in vivo . Results showed that treatment with insulin and the type‐A GABA receptor (GABA A R) agonist muscimol significantly decreased intracellular [Ca 2+ ], phosphorylated mammalian target of rapamycin (p‐mTOR) and proliferation rate in cultured αTC1‐6 cells. Administration of streptozotocin (STZ) to mice caused a substantial decrease of GAD and GABA as well as insulin in β‐cells at 12 hours, which was followed by an upsurge of p‐mTOR in α‐cells at day 1 and a significant increase of α‐cell mass at day 3. Remarkably, treatment with GABA largely restored the level of insulin and GAD in pancreatic β‐cells, and significantly decreased α‐cell mass and hyperglucagonemia in the STZ‐injected mice. These results show that decreases in GABA and insulin in β‐cells increase α‐cell proliferation in type 1 diabetes. We conclude that the paracrine GABA and insulin restrain α‐cell proliferation in islets of Langerhans. Support or Funding Information Canadian Institutes of Health Research (CIHR)
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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) | 0.004 | 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".