GABAergic regulation of pancreatic islet cells: Physiology and antidiabetic effects
Bibliographic record
Abstract
Diabetes occurs when pancreatic β-cell death exceeds β-cell growth, which leads to loss of β-cell mass. An effective therapy must have two actions: promotion of β-cell replication and suppression of β-cell death. Previous studies have established an important role for γ-aminobutyric acid (GABA) in islet-cell hormone homeostasis, as well as the maintenance of the β-cell mass. GABA exerts paracrine actions on α cells in suppressing glucagon secretion, and it has autocrine actions on β cells that increase insulin secretion. Multiple studies have shown that GABA increases the mitotic rate of β cells. In mice, following β-cell depletion with streptozotocin, GABA therapy can restore the β-cell mass. Enhanced β-cell replication appears to depend on growth and survival pathways involving Akt activation. Some studies have also suggested that it induces transdifferentiation of α cells into β cells, but this has been disputed and requires further investigation. In addition to proliferative effects, GABA protects β cells against injury and markedly reduces their apoptosis under a variety of conditions. The antiapoptotic effects depend at least in part on the enhancement of sirtuin-1 and Klotho activity, which both inhibit activation of the NF-κB inflammatory pathway. Importantly, in xenotransplanted human islets, GABA therapy stimulates β-cell replication and insulin secretion. Thus, the intraislet GABAergic system is a target for the amelioration of diabetes therapy, including β-cell survival and regeneration. GABA (or GABAergic drugs) can be combined with other antidiabetic drugs for greater effect.
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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".