Pituitary adenylate cyclase activating polypeptide enhances glucose-evoked insulin secretion in the canine pancreas in vivo.
Bibliographic record
Abstract
OBJECTIVE: To study a local effect of pituitary adenylate cyclase activating polypeptide (PACAP(1-27)) on glucose-evoked insulin release under in vivo conditions. INTERVENTION: Glucose and PACAP(1-27) were locally infused to the pancreas via the superior pancreaticoduodenal artery without interrupting the blood supply. MAIN OUTCOME MEASURES: Plasma insulin and glucose concentrations were determined in samples obtained from the superior pancreaticoduodenal vein and the aorta. Superior pancreaticoduodenal venous blood flow was measured to compute the net output of insulin. RESULTS: PACAP(1-27) (0.005-5 microg) increased the basal insulin secretion by about 15 folds in a dose-dependent manner. Local infusion of either glucose (5%) or PACAP(1-27) (0.05 microg) resulted in a significant increase in the basal insulin output to about 300 microU x min(-1)g(-1), which was highly reproducible upon the second administration of the same dose with an interval of 30 min. When PACAP(1-27) was simultaneously given during glucose infusion, the increased insulin output due to glucose was further enhanced to about 600 microU x min(-1)g(-1). The net increase in PACAP(1-27)-induced insulin output in the presence of glucose was significantly greater than that obtained with PACAP(1-27) alone. There exists a strong and highly significant correlation between changes in glucose level and those in insulin output when both glucose and PACAP(1-27) were administered simultaneously. CONCLUSION: The results indicate that PACAP(1-27) directly enhances the glucose-evoked insulin secretion in the endocrine pancreas in anesthetized dogs. The study suggests that PACAP may play a local facilitating role in insulin secretion in response to glucose loading.
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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 | 0.001 |
| 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".