A Method for Performing Islet Transplantation Using Tissue-Engineered Sheets of Islets and Mesenchymal Stem Cells
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) are known to have a protective effect on islet cells. Cell sheets developed using tissue engineering help maintain the function of the cells themselves. This study describes a tissue engineering approach using islets with MSC sheets to improve the therapeutic effect of islet transplantation. MSCs were obtained from Fischer 344 rats and engineered into cell sheets using temperature-responsive culture dishes. The islets obtained from Fischer 344 rats were seeded onto MSC sheets, and the islets with MSC sheets were harvested by low-temperature treatment after coculture. The functional activity of the islets with MSC sheets was confirmed by a histological examination, insulin secretion assay, and quantification of the levels of cytokines. The therapeutic effects of the islets with MSC sheets were investigated by transplanting the sheets at subcutaneous sites in severe combined immunodeficiency (SCID) mice with streptozotocin-induced diabetes. Improvement of islet function and viability was shown in situ on the MSC sheet, and the histological examination showed that the MSC sheet maintained adhesion factor on the surface. In the recipient mice, normoglycemia was maintained for at least 84 days after transplantation, and neovascularization was observed. These results demonstrated that islet transplantation in a subcutaneous site would be possible by using the MSC sheet as a scaffold for islets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".