Recombinant Reg3α Prevents Islet β-Cell Apoptosis and Promotes β-Cell Regeneration
Bibliographic record
Abstract
Progressive loss and dysfunction of islet β-cells has not yet been solved in the treatment of diabetes. Regenerating protein (Reg) has been identified as a trophic factor which is demonstrated to be associated with pancreatic tissue regeneration. We previously produced recombinant Reg3α protein (rReg3α) and proved that it protects against acute pancreatitis in mice. Whether rReg3α protects islet β-cells in diabetes has been elusive. In the present study, rReg3α stimulated MIN6 cell proliferation and resisted STZ-caused cell death. The protective effect of rReg3α was also found in mouse primary islets. In BALB/c mice, rReg3α administration largely alleviated STZ-induced diabetes by the preservation of β-cell mass. The protective mechanism could be attributed to Akt/Bcl-2/-xL activation and GRP78 upregulation. Scattered insulin-expressing cells and clusters with small size, low insulin density, and exocrine distribution were observed and considered to be neogenic. In isolated acinar cells with wheat germ agglutinin (WGA) labeling, rReg3α treatment generated insulin-producing cells through Stat3/Ngn3 signaling, but these cells were not fully functional in response to glucose stimulation. Our results demonstrated that rReg3α resists STZ-induced β-cell death and promotes β-cell regeneration. rReg3α could serve as a potential drug for β-cell maintenance in anti-diabetic treatment.
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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 | 0.000 | 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".