Bibliographic record
Abstract
Glucagon-like peptide 1 (GLP-1) is a gut-derived peptide secreted from intestinal L-cells after a meal. GLP-1 has numerous physiological actions, including potentiation of glucose-stimulated insulin secretion, enhancement of β-cell growth and survival, and inhibition of glucagon release, gastric emptying, and food intake. These antidiabetic effects of GLP-1 have led to intense interest in the use of this peptide for the treatment of patients with type 2 diabetes. Oral nutrients such as glucose and fat are potent physiological regulators of GLP-1 secretion, but non-nutrient stimulators of GLP-1 release have also been identified, including the neuromodulators acetylcholine and gastrin-releasing peptide. Peripheral hormones that participate in energy homeostasis, such as leptin, have also been implicated in the regulation of GLP-1 release. Recent studies have begun to elucidate the intracellular signaling pathways that mediate the effects of GLP-1 secretagogues on the intestinal L-cell. The purpose of this review is to summarize the known signaling mechanisms of GLP-1 secretagogues based on the available literature. A better understanding of the pathways underlying GLP-1 secretion may lead to novel approaches by which the levels of this important insulinotropic hormone can be enhanced in patients with type 2 diabetes.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".