Therapeutic Approaches to Preserve Islet Mass in Type 2 Diabetes
Bibliographic record
Abstract
Type 2 diabetes is characterized by hyperglycemia resulting from insulin resistance in the setting of inadequate beta-cell compensation. Currently available therapeutic agents lower blood glucose through multiple mechanisms but do not directly reverse the decline in beta-cell mass. Glucagon-like peptide-1 (GLP-1) receptor agonists, exemplified by Exenatide (exendin-4), not only acutely lower blood glucose but also engage signaling pathways in the islet beta-cell that lead to stimulation of beta-cell replication and inhibition of beta-cell apoptosis. Similarly, glucose-dependent insulinotropic polypeptide (GIP) receptor activation stimulates insulin secretion, enhances beta-cell proliferation, and reduces apoptosis. Moreover, potentiation of the endogenous postprandial levels of GLP-1 and GIP via inhibition of dipeptidyl peptidase-IV (DPP-IV) also expands beta-cell mass via related mechanisms. The thiazolidinediones (TZDs) enhance insulin sensitivity, reduce blood glucose levels, and also preserve beta-cell mass, although it remains unclear whether TZDs affect beta-cell mass via direct mechanisms. Complementary approaches to regeneration of beta-cell mass involve combinations of factors, exemplified by epidermal growth factor and gastrin, which promote islet neogenesis and ameliorate diabetes in rodent studies. Considerable preclinical data support the concept that one or more of these therapeutic approaches, alone or in combination, may potentially reverse the decline in beta-cell mass that is characteristic of the natural history of type 2 diabetes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".