GABAergic system in the endocrine pancreas: a new target for diabetes treatment
Bibliographic record
Abstract
Excessive loss of functional pancreatic β-cell mass, mainly due to apoptosis, is a major factor in the development of hyperglycemia in both type 1 and type 2 diabetes (T1D and T2D). In T1D, β-cells are destroyed by immunological mechanisms. In T2D, while metabolic factors are known to contribute to β-cell failure and subsequent apoptosis, mounting evidence suggests that islet inflammation also plays an important role in the loss of β-cell mass. Therefore, it is of great importance for clinical intervention to develop new therapies. γ-Aminobutyric acid (GABA), a major neurotransmitter, is also produced by islet β-cells, where it functions as an important intraislet transmitter in regulating islet-cell secretion and function. Importantly, recent studies performed in rodents, including in vivo studies of xenotransplanted human islets, reveal that GABA exerts β-cell regenerative effects. Moreover, it protects β-cells against apoptosis induced by cytokines, drugs, and other stresses, and has anti-inflammatory and immunoregulatory activities. It ameliorates the manifestations of diabetes in preclinical models, suggesting potential applications for the treatment of diabetic patients. This review outlines the actions of GABA relevant to β-cell regeneration, including its signaling mechanisms and potential interactions with other mediators. These studies increase our understanding of the regenerative processes of pancreatic β-cells, and help pave the way for the development of regenerative medicine for diabetes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".