Effects of somatostatin receptor type 2 antagonism during insulin‐induced hypoglycaemia in male rats with prediabetes
Bibliographic record
Abstract
AIMS: To examine if glucagon counterregulatory defects exist in a rat model of prediabetes (pre-T2D) and to assess if a selective somatostatin receptor 2 antagonist (SSTR2a), ZT-01, enhances the glucagon response to insulin-induced hypoglycaemia. MATERIALS AND METHODS: Hyperglycaemia was induced in 8- to 9-week-old male, Sprague-Dawley rats via 7 weeks of high-fat diet followed by a single, low-dose intraperitoneal injection of streptozotocin (30 mg/kg). After 2 weeks of basal insulin therapy (0-4 U/d insulin glargine, administered subcutaneously [SC]) to facilitate partial glycaemic recovery and a pre-T2D phenotype, n = 17 pre-T2D and n = 10 normal chow-fed control rats underwent the first of two hypoglycaemic treatment-crossover experiments, separated by a 1-week washout period. On each experimental day, SSTR2a (3 mg/kg ZT-01, SC) or vehicle was administered 1 hour prior to insulin-induced hypoglycaemia (insulin aspart, 6 U/kg, SC). RESULTS: Glucagon counterregulation was marginally reduced with the induction of pre-T2D. Treatment with SSTR2a raised peak plasma glucagon levels and glucagon area under the curve before and after insulin overdose in both and pre-T2D rats. Blood glucose concentration was elevated by 30 minutes after SSTR2a treatment in pre-T2D rats, and hypoglycaemia onset (≤3.9 mmol/L) was delayed by 15 ± 12 minutes compared with vehicle (P < 0.001), despite similar glucose nadirs in the two treatment groups (1.4 ± 0.3 mmol/L). SSTR2a treatment had no effect on blood glucose levels in the control group or on the hypoglycaemia-induced decline in plasma C-peptide levels in either group. CONCLUSIONS: Treatment with an SSTR2a increases glucagon responsiveness and delays the onset of insulin-induced hypoglycaemia in this rat model of pre-T2D where only a modest deficiency in glucagon counterregulation exists.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".