Somatostatin Receptor Type 2 Antagonism Improves Glucagon Counterregulation in Biobreeding Diabetic Rats
Bibliographic record
Abstract
Impaired counterregulation during hypoglycemia in type 1 diabetes (T1D) is partly attributable to inadequate glucagon secretion. Intra-islet somatostatin (SST) suppression of hypoglycemia-stimulated α-cell glucagon release plays an important role. We hypothesized that hypoglycemia can be prevented in autoimmune T1D by SST receptor type 2 (SSTR2) antagonism of α-cells, which relieve SSTR2 inhibition, thereby increasing glucagon secretion. Diabetic biobreeding diabetes-prone (BBDP) rats mimic insulin-dependent human autoimmune T1D, whereas nondiabetic BBDP rats mimic prediabetes. Diabetic and nondiabetic rats underwent a 3-h infusion of vehicle compared with SSTR2 antagonist (SSTR2a) during insulin-induced hypoglycemia clamped at 3 ± 0.5 mmol/L. Diabetic rats treated with SSTR2a needed little or no glucose infusion compared with untreated rats. We attribute this effect to SSTR2a restoration of the attenuated glucagon response. Direct effects of SSTR2a on α-cells was assessed by resecting the pancreas, which was cut into fine slices and subjected to perifusion to monitor glucagon release. SSTR2a treatment enhanced low-glucose-stimulated glucagon and corticosterone secretion to normal levels in diabetic rats. SSTR2a had similar effects in vivo in nondiabetic rats and promoted glucagon secretion from nondiabetic rat and human pancreas slices. We conclude that SST contributes to impaired glucagon responsiveness to hypoglycemia in autoimmune T1D. SSTR2a treatment can fully restore hypoglycemia-stimulated glucagon release sufficient to attain normoglycemia in both diabetic and prediabetic stages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".