Hepatocyte Growth Factor Gene Therapy for Pancreatic Islets in Diabetes: Reducing the Minimal Islet Transplant Mass Required in a Glucocorticoid-Free Rat Model of Allogeneic Portal Vein Islet Transplantation
Bibliographic record
Abstract
Islet transplantation for diabetes is limited by the availability of human islet donors. Hepatocyte growth factor (HGF) is a potent beta-cell mitogen and survival factor and improves islet transplant outcomes in a murine model. However, the murine model employs renal subcapsular transplant and immunodeficient mice, features not representative of human islet transplantation protocols. Therefore, we have developed a more rigorous, marginal-mass rat islet transplant model that more closely resembles human islet transplantation protocols: islet donors are allogeneic Lewis islets; recipients are normal Sprague Dawley rats; islets are delivered intraportally; and immunosuppression is accomplished using the same immunosuppressants employed by the Edmonton group. We demonstrate that 1) surprisingly, the Edmonton immunosuppression regimen induces marked insulin resistance and beta-cell toxicity in rats, 2) adenovirus does not adversely affect islet transplant outcomes, 3) the Edmonton immunosuppressants may delay or block rejection of adenovirally transduced islets, and more importantly, 4) pretransplant islet adenoviral gene therapy with HGF markedly improves islet transplant outcomes, 5) this enhanced function persists for months, and 6) HGF enhances islet function and survival even in the setting of immunosuppressant-induced insulin resistance and beta-cell toxicity. This approach may enhance islet transplantation outcomes in humans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".