A Century-long Journey From the Discovery of Insulin to the Implantation of Stem Cell–derived Islets
Bibliographic record
Abstract
For the past century, insulin injections have saved millions of lives, but glycemic instability is still a persistent challenge for people with diabetes, leading to tremendous morbidity and premature mortality. Research in the field of islet transplantation has demonstrated that replacing insulin-producing β cells can restore euglycemia comparable to individuals without diabetes. However, a short supply of cadaveric islet donors, the technically challenging process of isolating islets, and the requirement for chronic immune suppression have impeded widespread clinical adoption. Rather than relying on cadaveric cells, pluripotent stem cells could serve as a virtually unlimited supply of insulin-producing β cells. Protocols have been developed that mimic the normal in vivo development of the human pancreas to generate pancreatic progenitor cells in vitro. Ongoing investigations have yielded progressively more mature β-like cells in vitro that produce insulin but do not yet fully mimic healthy mature β cells. Alongside development of differentiation protocols, other work has provided insight into potential implantation sites for stem cell-derived islet cells including the subcutaneous space, portal vein, and omentum. To optimize implanted cell survival and function, development of immune modulation therapies is ongoing, including selection of immunomodulatory medications and genetic modification of implanted cells to evade immune responses. Further, macroencapsulation or microencapsulation devices could be used to contain and/or immunoprotect implanted cells from the immune response including by using 3-dimensional bioprinting to facilitate the process. Remarkably, ongoing clinical trials have now yielded the first patient relying on differentiated stem cells rather than syringes as their insulin replacement therapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".