Triple drug therapy with GABA, sitagliptin, and omeprazole prevents type 1 diabetes onset and promotes its reversal in non-obese diabetic mice
Bibliographic record
Abstract
Previous studies have reported that dual drug combinations consisting of γ-aminobutyric acid (GABA) together with a dipeptidyl-peptidase-4 inhibitor (DPP-4i), also a DPP-4i with a proton pump inhibitor (PPI), could improve pancreatic β-cell function and ameliorate diabetes in diabetic mice. In this study, we sought to determine if a triple drug combination of GABA, a DPP-4i and a PPI might have superior therapeutic effects compared with double drug therapies in the prevention and reversal of diabetes in the non-obese diabetic (NOD) mouse model of human type 1 diabetes (T1D). In a diabetes prevention arm of the study, the triple drug combination of GABA, a DPP-4i, and a PPI exhibited superior therapeutic effects in preventing the onset of diabetes compared with all the double drug combinations and placebo. Also, the triple drug combination significantly increased circulating C-peptide and serum insulin levels in the mice. In a diabetes reversal arm of the study, the triple drug combination was superior to all of the double drug combinations in reducing hyperglycemia in the mice. In addition, the triple drug combination was the most effective in increasing circulating levels of C-peptide and serum insulin, thereby significantly reducing exogenous insulin needs. The combination of GABA, a DPP-4i and a PPI appears to be a promising and easily scalable therapy for the treatment and prevention of T1D.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".