The multifaceted potential of glucagon-like peptide-1 as a therapeutic agent.
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1), an intestinal gut hormone, is rapidly emerging as a new therapeutic agent for the treatment of diabetes mellitus. GLP-1, released from intestinal L-cells, is renowned for its potent stimulation of insulin biosynthesis and release from pancreatic b-cells. Exogenous administration of GLP-1 to subjects with type 2 diabetes results in the normalization of plasma glucose concentrations, in part, as a result of augmented glucose-stimulated insulin secretion. However, it is now recognized that GLP-1 has several other anti-diabetic actions that collectively improve the type 2 diabetic phenotype, and may also prove beneficial in the treatment of type 1 diabetes. These effects include the deceleration of gastric emptying and promotion of satiety, thereby reducing the availability of nutrients for absorption and reducing the requirement for insulin secretion. GLP-1 also reduces plasma glucose levels by suppressing glucagon secretion from pancreatic a-cells and potentially by improving insulin sensitivity in peripheral tissues. Further-more, GLP-1 upregulates expression of b-cell genes (GLUT2, glucokinase, insulin, and PDX-1) and promotes b-cell neogenesis and differentiation of ductal cells into insulin secreting cells. Although initial clinical trials indicate GLP-1 has excellent therapeutic potential, its relatively short-lived biological activity and delivery difficulties limit its appeal. Several approaches that are currently being explored to overcome these limitations include mobilizing endogenous GLP-1 release, preserving the biological activity of the native peptide, and developing GLP-1 analogues with extended durations of action.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".