Hepatocyte growth factor gene therapy for islet transplantation
Bibliographic record
Abstract
Recent clinical studies have documented that human islet transplantation has the potential to replace pancreatic endocrine function in patients with type 1 diabetes. These studies have also highlighted an enormous shortage of human islets that impedes the use of islet transplantation in clinical practice on a larger scale. To address this problem, one potential approach is to use islet growth factors to increase beta cell replication, to improve beta cell function and to enhance beta cell survival. In that context, transgenic mice overexpressing hepatocyte growth factor (HGF) in the pancreatic beta cell display increased beta cell proliferation, function and survival. More importantly, HGF-overexpressing transgenic mouse islets markedly improve transplant performance in severe combined immunodeficiency (SCID) mice and reduce the number of islets required for successful islet transplantation. Recently, adenoviral-mediated gene transfer of HGF into normal rodent islets has confirmed the beneficial effects of HGF in improving islet transplant outcomes in two marginal mass islet transplant models in rodents: islet transplant under the kidney capsule in SCID mice; and portal islet allograft transplantation in rats treated with the Edmonton immunosuppressive regimen. These studies suggest that ex vivo HGF gene therapy has the potential to reduce the number of human islets required for successful islet transplantation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".