Role of the Pancreatic Islet Microvasculature in Health and Disease
Bibliographic record
Abstract
The pancreatic islet vasculature comprises microvascular endothelial cells surrounded by mural cells (pericytes). Both cell types support the islet by providing (1) a conduit for delivery and exchange of nutrients and hormones; (2) paracrine signals and extracellular matrix (ECM) components that support islet development, architecture, and endocrine function; and (3) a barrier against inflammation and immune cell infiltration. In type 2 diabetes, the islet vasculature becomes inflamed, showing loss of endothelial cells, detachment, and/or trans-differentiation of pericytes, vessel dilation, and excessive ECM deposition. While most work to date has focused either on endothelial cells or pericytes in isolation, it is very likely that the interaction between these cell types and disruption of that interaction in diabetes are critically important. In fact, dissociation of pericytes from endothelial cells is an early, key feature of microvascular disease in multiple tissues/disease states. Moreover, in beta-cell replacement therapy, co-transplantation with microvessels versus endothelial cells alone is substantially more effective in improving survival and function of the transplanted cells. Ongoing studies, including characterization of islet vascular cell signatures, will aid in the identification of new therapeutic targets aimed at improving islet function and benefiting people living with all forms of 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 | 0.002 | 0.002 |
| 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) | 0.003 | 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".