Combined use of GABA and sitagliptin promotes human β-cell proliferation and reduces apoptosis
Bibliographic record
Abstract
γ-Aminobutyric acid (GABA) and glucagon-like peptide-1 receptor agonist (GLP-1RA) improve rodent β-cell survival and function. In human β-cells, GABA exerts stimulatory effects on proliferation and anti-apoptotic effects, whereas GLP-1RA drugs have only limited effects on proliferation. We previously demonstrated that GABA and sitagliptin (Sita), a dipeptidyl peptidase-4 inhibitor which increases endogenous GLP-1 levels, mediated a synergistic β-cell protective effect in mice islets. However, it remains unclear whether this combination has similar effects on human β-cell. To address this question, we transplanted a suboptimal mass of human islets into immunodeficient NOD-scid-gamma mice with streptozotocin-induced diabetes, and then treated them with GABA, Sita, or both. The oral administration of either GABA or Sita ameliorated blood glucose levels, increased transplanted human β-cell counts and plasma human insulin levels. Importantly, the combined administration of the drugs generated significantly superior results in all these responses, as compared to the monotherapy with either one of them. The proliferation and/or regeneration, improved by the combination, were demonstrated by increased Ki67+, PDX-1+, or Nkx6.1+ β-cell numbers. Protection against apoptosis was also significantly improved by the drug combination. The expression level of α-Klotho, a protein with protective and stimulatory effects on β cells, was also augmented. Our study indicates that combined use of GABA and Sita produced greater therapeutic benefits, which are likely due to an enhancement of β-cell proliferation and a decrease in apoptosis.
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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 | 0.000 |
| 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".