Lung-Derived Microscaffolds Facilitate Diabetes Reversal after Mouse and Human Intraperitoneal Islet Transplantation
Bibliographic record
Abstract
There is a need to develop three-dimensional structures that mimic the natural islet tissue microenvironment. Endocrine micro-pancreata (EMPs) made up of acellular organ-derived micro-scaffolds seeded with human islets have been shown to express high levels of key beta-cell specific genes and secrete quantities of insulin per cell similar to freshly isolated human islets in a glucose-regulated manner for more than three months in vitro. The aim of this study was to investigate the capacity of EMPs to restore euglycemia in vivo after transplantation of mouse or human islets in chemically diabetic mice. We proposed that the organ-derived EMPs would restore the extracellular components of the islet microenvironment, generating favorable conditions for islet function and survival. EMPs seeded with 500 mouse islets were implanted intraperitoneally into streptozotocin-induced diabetic mice and reverted diabetes in 67% of mice compared to 13% of controls (p = 0.018, n = 9 per group). Histological analysis of the explanted grafts 60 days post-transplantation stained positive for insulin and exhibited increased vascular density in a collagen-rich background. EMPs were also seeded with human islets and transplanted into the peritoneal cavity of immune-deficient diabetic mice at 250 islet equivalents (IEQ), 500 IEQ and 1000 IEQ. Escalating islet dose increased rates of normoglycemia (50% of the 500 IEQ group and 75% of the 1000 IEQ group, n = 3 per group). Human c-peptide levels were detected 90 days post-transplantation in a dose-response relationship. Herein, we report reversal of diabetes in mice by intraperitoneal transplantation of human islet seeded on EMPs with a human islet dose as low as 500 IEQ.
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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".