Amelioration of Hypoglycemia Via Somatostatin Receptor Type 2 Antagonism in Recurrently Hypoglycemic Diabetic Rats
Bibliographic record
Abstract
Selective antagonism of somatostatin receptor type 2 (SSTR2) normalizes glucagon and corticosterone responses to hypoglycemic clamp in diabetic rats. The purpose of this study was to determine whether SSTR2 antagonism (SSTR2a) ameliorates hypoglycemia in response to overinsulinization in diabetic rats previously exposed to recurrent hypoglycemia. Streptozotocin diabetic rats (n = 19), previously subjected to five hypoglycemia events over 3 days, received an insulin bolus (10 units/kg i.v.) plus insulin infusion (50 mU/kg/min i.v.) until hypoglycemia ensued (≤3.9 mmol/L) (experimental day 1 [Expt-D1]). The next day (Expt-D2), rats were allocated to receive either placebo treatment (n = 7) or SSTR2a infusion (3,000 nmol/kg/min i.v., n = 12) 60 min prior to the same insulin regimen. On Expt-D1, all rats developed hypoglycemia by ∼90 min, while on Expt-D2, hypoglycemia was attenuated with SSTR2a treatment (nadir = 3.7 ± 0.3 vs. 2.7 ± 0.3 mmol/L in SSTR2a and controls, P < 0.01). Glucagon response to hypoglycemia on Expt-D2 deteriorated by 20-fold in the placebo group (P < 0.001) but improved in the SSTR2a group (threefold increase in area under the curve [AUC], P < 0.001). Corticosterone response deteriorated in the placebo-treated rats on Expt-D2 but increased twofold in the SSTR2a group. Catecholamine responses were not affected by SSTR2a. Thus, SSTR2 antagonism after recurrent hypoglycemia improves the glucagon and corticosterone responses and largely ameliorates insulin-induced hypoglycemia in diabetic rats.
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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 | 0.000 |
| 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".