Transplantation Models to Characterize the Mechanisms of Stem Cell–Induced Islet Regeneration
Bibliographic record
Abstract
This unit describes our current knowledge regarding the isolation human bone marrow-derived progenitor cells for the paracrine stimulation of islet regeneration after transplantation into immunodeficient mouse models of diabetes. By using high aldehyde dehydrogenase (ALDH(hi) ) activity, a conserved function in multiple stem cell lineages, a mixed population of hematopoietic, endothelial, and mesenchymal progenitor cells can be efficiently purified using flow cytometry. We describe in vitro approaches to characterize and expand these distinct cell types. Importantly, these cell types can be transplanted into immunodeficient mice rendered beta-cell deficient by streptozotocin (STZ) treatment, in order monitor functional recovery from hyperglycemia and to characterize endogenous islet regeneration via paracrine mechanisms. Herein, we provide detailed protocols for: (1) isolation and characterization of ALDH(hi) cells for the establishment of hematopoietic and multipotent-stromal progenitor lineages; (2) intravenous and intrapancreatic transplantation of human stem cell subtypes for the quantification of glycemic recovery in STZ-treated immunodeficient mice; and (3) immunohistochemical characterization of islet recovery via the stimulation of islet neogenic, beta-cell proliferative, and islet revascularization programs. Collectively, these systems can be used to support the pre-clinical development of human progenitor cell-based therapies to treat diabetes via islet regeneration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".