In vivo models of gestational and type 2 diabetes mellitus characterized by endocrine pancreas cell impairments
Bibliographic record
Abstract
Insulin resistance contributes to the development of various diseases, including type 2 diabetes and gestational diabetes. Even though gestational diabetes is specific to pregnancy, it can result in long-term glucose intolerance and type 2 diabetes after delivery. Given the substantial health and economic burdens associated with diabetes, it is imperative to better understand the mechanisms leading to insulin resistance and type 2 diabetes so that treatments targeted at reversing symptoms can be developed. Considering that the endocrine cells of the pancreas (islets of Langerhans) largely contribute to the pathogenesis of diabetes (beta-cell insufficiency and dysfunction), the elucidation of the various mechanisms of endocrine cell plasticity is important to understand. By better defining these mechanisms, targeted therapeutics can be developed to reverse symptoms of beta-cell deficiency and insulin resistance in diabetes. Animal models play an important role in better understanding these mechanisms, as techniques for in vivo imaging of endocrine cells in the pancreas are limited. Therefore, this review article will discuss the available rodent models of gestational and type 2 diabetes that are characterized by endocrine cell impairments in the pancreas, discuss the models with a comparison to human diabetes, and explore the potential mechanisms of endocrine cell plasticity that contribute to these phenotypes, as these mechanisms could ultimately be used to reverse blood glucose dysregulation in 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 | 0.001 |
| 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) | 0.005 | 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".