Gene Transfection and Expression of Transforming Growth Factor-β1 in Nonobese Diabetic Mouse Islets Protects β-Cells in Syngeneic Islet Grafts from Autoimmune Destruction
Bibliographic record
Abstract
Nonobese diabetic (NOD) mice develop diabetes and destroy syngeneic islet grafts through an autoimmune response. Because transforming growth factor (TGF)-beta1 downregulates immune responses, we tested whether overexpression of TGF-beta1 by gene transfection of NOD mouse islets could protect beta-cells in islet grafts from autoimmune destruction. NOD mouse islet cells were transfected with an adenoviral DNA expression vector encoding porcine latent TGF-beta1 (Ad TGF-beta1) or the adenoviral vector alone (control Ad vector). The frequency of total islet cells expressing TGF-beta1 protein was increased from 12 +/- 1% in control Ad vector-transfected cells to 89 +/- 4% in Ad TGF-beta1-transfected islet cells, and the frequency of beta-cells that expressed TGF-beta1 was increased from 12 +/- 1% to 60 +/- 7%. Also, secretion of TGF-beta1 was significantly increased in islets that overexpressed TGF-beta1. Ad TGF-beta1-transfected NOD mouse islets that overexpressed TGF-beta1 prevented diabetes recurrence after transplantation into diabetic NOD mice for a median of 22 days compared with only 7 days for control Ad vector-transfected islets (p = 0.001). Immunohistochemical examination of the islet grafts revealed significantly more TGF-beta1+ cells and insulin+ cells and significantly fewer CD45+ leukocytes in Ad TGF-beta1-transfected islet grafts. Also, islet beta-cell apoptosis was significantly decreased whereas apoptosis of graft-infiltrating leukocytes was significantly increased in Ad TGF-beta1-transfected islet grafts. These observations demonstrate that overexpression of TGF-beta1, by gene transfection of NOD mouse islets, protects islet beta-cells from apoptosis and autoimmune destruction and delays diabetes recurrence after islet transplantation.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".