Co‐transplantation of human adipose‐derived mesenchymal stem cells with neonatal porcine islets within a prevascularized subcutaneous space augments the xenograft function
Bibliographic record
Abstract
BACKGROUND: Cell transplantation has been widely recognized as a curative treatment strategy for variety of diseases including type I diabetes (T1D). Broader patient inclusion for this therapeutic option is restricted by a limited supply of healthy human islet donors and significant loss of islets immediately postintrahepatic transplant due to immune activation. Neonatal porcine islets (NPIs) are a potential ubiquitous β-cell source for treating T1D. Mesenchymal stem cells (MSCs) have the inherent capacity to secrete immunoregulatory, anti-inflammatory, and proangiogenic factors and, thus, have the potential to improve islet engraftment, survival, and function. METHODS: Herein, we assessed the effect of human adipose-derived MSCs (AdMSCs) on NPI metabolic outcomes in diabetic mice when co-transplanted within the prevascularized subcutaneous deviceless (DL) space or kidney capsule (KC). Graft function has been evaluated by weekly blood glucose, stimulated porcine insulin, glucose tolerance, and total cellular graft insulin content. RESULTS: Compared with NPI alone, co-transplantation of NPIs and AdMSCs resulted in significantly earlier normoglycemia (*P < .05), improved glucose tolerance (*P < .05), superior stimulated serum porcine insulin (**P < .01), and increased graft insulin content (*P < .05) in the DL site and not the KC. CONCLUSIONS: Thus, our study demonstrates that co-transplantation of human AdMSCs with NPIs is an effective tactic to augment islet xenograft function in a clinically relevant extrahepatic site.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".