β-Cell Replication by Loosening the Brakes of Glucagon-Like Peptide-1 Receptor Signaling
Bibliographic record
Abstract
A decrease in β-cell mass is a well-known key pathogenic event in diabetes, not only in human subjects with type 1 patients, where β-cells are destroyed by the immune system, but also in type 2 diabetes where reduced β-cell function results in hyperglycemia and associated metabolic abnormalities (1,2). These concepts have made the search for the set of rules that control pancreatic β-cell mass an important area of islet research. An interesting emerging topic with clinical relevance is the notion that the actions of glucagon-like peptide-1 (GLP-1) on islet β-cells could be harnessed to improve and preserve β-cell function and potentially reverse defects in β-cell mass (3). GLP-1 is a proglucagon-derived peptide secreted from gut endocrine cells that acts on β-cells at multiple levels, acutely stimulating insulin secretion while chronically promoting proinsulin biosynthesis and growth and survival of β-cells. During meals, GLP-1 is secreted and acts immediately as an incretin, acutely potentiating glucose-dependent insulin release. However, GLP-1 also enhances glucose competence of β-cells and restores glucose sensitivity to diabetic β-cells in vivo. These findings, taken together with the clinical development of GLP-1 receptor (GLP-1R) agonists and dipeptidyl peptidase-4 inhibitors, have focused on attention to the extent to which incretin-based agents may exert long-term beneficial effects on preservation of β-cell function in subjects with type 2 diabetes (4). In an exciting study published in this issue of Diabetes (5), two mechanisms by which GLP-1 causes β-cell replication have been explored. As the authors state, “the overall effect of GLP-1 on increasing β-cell mass in both in vivo and in vitro conditions is relatively small, and augmenting this effect would be beneficial for the treatment or prevention of both type 1 and type 2 diabetes.” The goal of the study by Klinger et al. was to elucidate molecular mechanisms …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".