Bibliographic record
Abstract
The ability of glucose to stimulate insulin secretion from the pancreatic islets of Langerhans is enhanced by the intestinal hormone glucagon-like peptide 1 (GLP-1), which is secreted from the gut in response to nutrient ingestion. This action, called the incretin effect, accounts for as much as half of the postprandial insulin response and is exploited therapeutically for diabetes treatment through the use of incretin mimetic drugs and inhibitors of dipeptidyl peptidase 4, which degrades GLP-1. Despite a prominent role for incretin mimetics in diabetes treatment, several key questions remain about GLP-1-induced insulin secretion. Most studies have examined the effects of GLP-1 at concentrations several orders of magnitude higher than those found in vivo; therefore, one might question the physiological (and perhaps even pharmacological) relevance of pathways identified in these studies and whether other important mechanisms might have been obscured. In this issue of the JCI, Shigeto and colleagues demonstrate that physiological GLP-1 does indeed amplify the insulin secretory response. Intriguingly, while much of this response is PKA dependent, as might be expected, the use of picomolar GLP-1 reveals a new and important mechanism that contributes to GLP-1-induced insulin secretion.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".