Gene Therapy to Improve Pancreatic Islet Transplantation for Type 1 Diabetes Mellitus
Bibliographic record
Abstract
Pancreatic islet transplantation is a promising treatment option for Type 1 Diabetics, offering improved glycaemic control through restoration of insulin production and freedom from life-threatening hypoglycaemic episodes. Implementation of the Edmonton protocol in 2000, a glucocorticoid-free immunosuppressive regimen has led to improved islet transplantation success. > 50% of islets are lost post-transplantation primarily through cytokine-mediated apoptosis, ischemia and hypoxia. Gene therapy presents a novel strategy to modify islets for improved survival post-transplantation. Current islet gene therapy approaches aim to improve islet function, block apoptosis and inhibit rejection. Gene transfer vectors include adenoviral, adeno-associated virus, herpes simplex virus vectors, retroviral vectors (including lentiviral vectors) and non-viral vectors. Adeno-associated virus is currently the best islet gene therapy vector, due to the vectors minimal immunogenicity and high safety profile. In animal models, using viral vectors to deliver genes conferring local immunoregulation, anti-apoptotic genes or angiogenic genes to islets can significantly improve islet survival in the early post-transplant period and influence long term engraftment. With recent improvements in gene delivery and increased understanding of the mechanisms underlying graft failure, gene therapy for islet transplantation has the potential to move closer to the clinic as a treatment for patients with Type 1 Diabetes. Keywords: Gene therapy, Islet transplantation, Type 1 diabetes, Viral vectors, Immunomodulatory, Anti-apoptotic, Angiogenic, Pancreatic Islet Transplantation, Diabetes Mellitus, cytokine-mediated apoptosis, adenoviral, adeno-associated virus, herpes simplex virus vectors, retroviral vectors, non-viral vectors, Apoptosis Protein, Islet Gene Therapy, Angiogenic Genes, therapeutic technique, AdV-based vectors
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".