Dimorphic autoantigenic and protective effects of Reg2 peptide in the treatment of diabetic β‐cell loss
Bibliographic record
Abstract
AIMS: The potential effect of regenerating (Reg) proteins in the treatment of diabetes has been indicated in the past decade, but the clinical use of Reg proteins requires more advances in translational medicine. In the present study, we produced recombinant regenerating protein 2 (rReg2), to prove its protective effect against streptozocin (STZ)-induced diabetes in BALB/c mice. MATERIALS AND METHODS: rReg2 was administrated in STZ-induced diabetic mice. Blood glucose, body weight, serum insulin and islet β-cell loss were determined. However, Reg2 has also been reported to serve as an autoantigen that induces autoimmune attacks on islets and aggravates diabetic development in non-obese diabetic mice. To address this contradiction, complete Freund's adjuvant was injected to generate a model that was hypersensitive to Reg2. In this model, islet CD8 T-cell infiltration, serum Reg2 antibody and interleukin (IL)-4 and IL-10, and splenic CD4+/interferon (IFN)-γ+ T cells were determined. RESULTS: Direct rReg2 pretreatment preserved islet β-cell mass against STZ and improved glycaemia, body weight and serum insulin content. The protection against cell death was further confirmed in cultured mouse islets and MIN6 cells. On the other hand, significant elevations of serum Reg2 antibody and splenic CD4+/IFN-γ+ T cells, and decreases in serum IL-4 and IL-10 were detected in rReg2-vaccinated mice, which may contribute to the accelerated diabetes. Interestingly, these mice, upon further rReg2 treatment, exhibited alleviated diabetic conditions with less islet CD8+ T-cell infiltration. CONCLUSION: rReg2 treatment ameliorated STZ-induced diabetes in normal BALB/c mice. By contrast, rReg2 vaccination exacerbated, but further rReg2 treatment alleviated, the severity of STZ-induced diabetes. Thus, the protective effect of rReg2 is predominant over the autoantigenic β-cell destruction, supporting the potential of rReg2 in the clinical treatment of diabetes.
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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 | 0.000 |
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 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".