Reg3α Overexpression Protects Pancreatic β Cells from Cytokine-Induced Damage and Improves Islet Transplant Outcome
Bibliographic record
Abstract
The process of islet transplantation for treating type 1 diabetes has been limited by the high level of graft failure. This may be overcome by locally delivering trophic factors to enhance engraftment. Regenerating islet-derived protein 3α (Reg3α) is a pancreatic secretory protein which functions as an antimicrobial peptide in control of inflammation and cell proliferation. In this study, to investigate whether Reg3α could improve islet engraftment, a marginal mass of syngeneic islets pretransduced with adenoviruses expressing Reg3α or control EGFP were transplanted under the renal capsule of streptozotocin-induced diabetic mice. Mice receiving islets with elevated Reg3α production exhibited significantly lower blood glucose levels (9.057 ± 0.59 mmol/L versus 13.48 ± 0.35 mmol/L, P < 0.05) and improved glucose-stimulated insulin secretion (1.80 ± 0.17 ng/mL versus 1.16 ± 0.16 ng/mL, P < 0.05) compared with the control group. The decline of apoptotic events (0.57% ± 0.15% versus 1.06% ± 0.07%, P < 0.05) and increased β-cell proliferation (0.70% ± 0.10% versus 0.36% ± 0.14%, P < 0.05) were confirmed in islet grafts overexpressing Reg3α by morphometric analysis. Further experiments showed that Reg3α production dramatically protected cultured islets and pancreatic β cells from cytokine-induced apoptosis and the impairment of glucose-stimulated insulin secretion. Moreover, exposure to cytokines led to the activation of MAPKs in pancreatic β cells, which was reversed by Reg3α overexpression in contrast to control group. These results strongly suggest that Reg3α could enhance islet engraftments through its cytoprotective effect and advance the therapeutic efficacy of 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".