Combined effect of GABA and glucagon‐like peptide‐1 receptor agonist on cytokine‐induced apoptosis in pancreatic β‐cell line and isolated human islets
Bibliographic record
Abstract
BACKGROUND: Treatment with GABA or glucagon-like peptide-1 (GLP-1) can preserve pancreatic β-cell mass and prevent diabetes. Recently, we reported that the combination of GABA and sitagliptin (a dipeptidyl peptidase-4 inhibitor that increases endogenous GLP-1) was more effective than either agent alone in reducing drug-induced β-cell damage and promoting β-cell regeneration in mice. However, in human islets, it remains unclear whether GABA and GLP-1 exert similar effects. METHODS: To investigate GABA and GLP-1 interactions, human islets or INS-1 cells were treated with GABA and/or exendin-4, a GLP-1 receptor agonist (GLP-1RA) in clinical use, and incubated with a cytokine mixture for 24 hours. Cleaved caspase-3 and annexin V binding were measured by western blot and flow cytometry analysis, respectively, to investigate effects on cytokine-induced apoptosis. RESULTS: Cytokine-induced apoptosis was reduced by either GABA or exendin-4 alone. This was markedly improved by combining GABA and exendin-4, resulting in a reversal of apoptosis. The combination notably increased Akt pathway signaling. Furthermore, sirtuin-1 (SIRT1) and α-Klotho, both reported to have protective effects on β-cells, were increased. Importantly, the combination ameliorated insulin secretion by human β-cells. CONCLUSIONS: The combination of GABA and a GLP-1RA exerted additive effects on β-cell survival and function, suggesting that this combination may be superior to either drug alone in the treatment of 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".