Insulin-expressing engineered cell lines and primary cells: surrogate β cells from liver, gut, and other sources
Bibliographic record
Abstract
PURPOSE OF REVIEW: Islet transplantation is being used to treat type 1 diabetes but is currently limited by the shortage of tissue available and by insufficient long-term function of transplanted islets. Thus, there remains significant interest in developing substitute sources of insulin-producing cells. Here we review progress in this area, focusing on insulin gene therapy and generation of new insulin-producing cells by redirecting hepatic and intestinal tissues towards a β-cell phenotype. RECENT FINDINGS: Insulin gene therapy using non-β cells has been improved by utilizing modified insulin constructs controlled by regulatory elements to confer nutrient responsiveness, and by inducing insulin production in endocrine cells that are equipped for rapid and in some cases glucose-responsive secretion. Significant advances have also been made towards generation of insulin-producing cells via transcriptional manipulation of hepatic and intestinal cells. These approaches offer the potential of generating a virtually limitless supply of insulin-producing cells. SUMMARY: The major challenge associated with insulin gene therapy in non-β cells is to achieve rapid, glucose-responsive secretion, while transdifferentiation approaches require additional characterization of the function and stability of insulin-producing cells. Continued efforts in these areas are warranted, as re-establishment of endogenous insulin production would be a welcome replacement to insulin injections for diabetes treatment.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".