Maturation of Human Embryonic Stem Cell–Derived Pancreatic Progenitors Into Functional Islets Capable of Treating Pre-existing Diabetes in Mice
Bibliographic record
Abstract
Diabetes is a chronic debilitating disease that results from insufficient production of insulin from pancreatic β-cells. Islet cell replacement can effectively treat diabetes but is currently severely limited by the reliance upon cadaveric donor tissue. We have developed a protocol to efficiently differentiate commercially available human embryonic stem cells (hESCs) in vitro into a highly enriched PDX1+ pancreatic progenitor cell population that further develops in vivo to mature pancreatic endocrine cells. Immature pancreatic precursor cells were transplanted into immunodeficient mice with streptozotocin-induced diabetes, and glycemia was initially controlled with exogenous insulin. As graft-derived insulin levels increased over time, diabetic mice were weaned from exogenous insulin and human C-peptide secretion was eventually regulated by meal and glucose challenges. Similar differentiation of pancreatic precursor cells was observed after transplant in immunodeficient rats. Throughout the in vivo maturation period hESC-derived endocrine cells exhibited gene and protein expression profiles that were remarkably similar to the developing human fetal pancreas. Our findings support the feasibility of using differentiated hESCs as an alternative to cadaveric islets for treating patients with 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 | 0.001 |
| 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".