SIRT1 activation attenuates α cell hyperplasia, hyperglucagonaemia and hyperglycaemia in STZ-diabetic mice
Bibliographic record
Abstract
-dependent lysine deacetylase, Sirtuin 1 (SIRT1), plays a central role in metabolic regulation. With type 1 diabetes a disease that is characterised by metabolic dysregulation, we sought to assess the impact of SIRT1 activation in experimental, streptozotocin (STZ)-induced diabetes. CD1 mice with and without STZ-induced diabetes were randomized to receive the SIRT1 activating compound, SRT3025, or vehicle over 20 weeks. Vehicle treated STZ-CD1 mice developed severe hyperglycaemia with near-absent circulating insulin and widespread beta cell loss in association with hyperglucagonaemia and expanded islet alpha cell mass. Without affecting ß-cell mass or circulating insulin, diabetic mice that received SRT3025 had substantially improved glycaemic control with greatly reduced islet α cell mass and lower plasma glucagon concentrations. Consistent with reduced glucagon abundance, the diabetes-associated overexpression of key gluconeogenic enzymes, glucose-6-phosphatase and PEPCK were also lowered by SRT3025. Incubating cultured α cells with SRT3025 diminished their glucagon secretion and proliferative activity in association with a reduction in the α cell associated transcription factor, Aristaless Related Homeobox (Arx). By reducing the paradoxical increase in glucagon, SIRT1 activation may offer a new, α-cell centric approach to the treatment of type 1 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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".