Transplanted Human Bone Marrow Progenitor Subtypes Stimulate Endogenous Islet Regeneration and Revascularization
Bibliographic record
Abstract
Transplanted murine bone marrow (BM) progenitor cells recruit to the injured pancreas and induce endogenous beta cell proliferation to improve islet function. To enrich for analogous human progenitor cell types that stimulate islet regeneration, we purified human BM based on high-aldehyde dehydrogenase activity (ALDH(hi)), an enzymatic function conserved in hematopoietic, endothelial, and mesenchymal progenitor lineages. We investigated the contributions of ALDH(hi) mixed progenitor cells or culture-expanded, ALDH-purified multipotent stromal cell (MSC) subsets to activate endogenous programs for islet regeneration after transplantation into streptozotocin-treated NOD/SCID mice. Intravenous injection of uncultured BM ALDH(hi) cells improved systemic hyperglycemia and augmented insulin secretion by increasing islet size and vascularization, without increasing total islet number. Augmented proliferation within regenerated endogenous islets and associated vascular endothelium indicated the induction of islet-specific proliferative and pro-angiogenic programs. Although cultured MSC from independent human BM samples showed variable capacity to improve islet function, and prolonged expansion diminished hyperglycemic recovery, transplantation of ALDH-purified regenerative MSC reduced hyperglycemia and augmented total beta cell mass by stimulating the formation of small beta cell clusters associated with the ductal epithelium, without evidence of increased islet vascularization or Ngn3(+) endocrine precursor activation. Thus, endogenous islet recovery after progenitor cell transplantation can occur via distinct regenerative mechanisms modulated by subtypes of progenitor cells administered. Further, understanding of how these islet regenerative and pro-angiogenic programs are activated by specific progenitor subsets may provide new approaches for combination cellular therapies to combat 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 | 0.000 | 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) | 0.002 | 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".