Modular tissue engineering for the vascularization of subcutaneously transplanted pancreatic islets
Bibliographic record
Abstract
The transplantation of pancreatic islets, following the Edmonton Protocol, is a promising treatment for type I diabetics. However, the need for multiple donors to achieve insulin independence reflects the large loss of islets that occurs when islets are infused into the portal vein. Finding a less hostile transplantation site that is both minimally invasive and able to support a large transplant volume is necessary to advance this approach. Although the s.c. site satisfies both these criteria, the site is poorly vascularized, precluding its utility. To address this problem, we demonstrate that modular tissue engineering results in an s.c. vascularized bed that enables the transplantation of pancreatic islets. In streptozotocin-induced diabetic SCID/beige mice, the injection of 750 rat islet equivalents embedded in endothelialized collagen modules was sufficient to restore and maintain normoglycemia for 21 days; the same number of free islets was unable to affect glucose levels. Furthermore, using CLARITY, we showed that embedded islets became revascularized and integrated with the host's vasculature, a feature not seen in other s.c. STUDIES: (M2-like) macrophage response, which was a feature of module-associated vascularization. While these results open the potential for using s.c. islet delivery as a treatment option for type I diabetes, the more immediate benefit may be for the exploration of revascularized islet biology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".