Controversial Roles of Regenerating Family Proteins in Tissue Repair and Tumor Development
Bibliographic record
Abstract
Background: Over the past 40 years since the discovery of regenerating family proteins (Reg proteins), numerous studies have highlighted their biological functions in promoting cell proliferation and resisting cell apoptosis, particularly in the regeneration and repair of pancreatic islets and exocrine glands. Successively, short peptides derived from Reg3δ and Reg3α have been employed in clinical trials, showing favorable therapeutic effects in patients with type I and type II diabetes. However, continued reports have been limited, presumably attributed to the potential side effects. Methods: This review summarizes extensive research on Reg proteins over the past decade, combined with our own related studies, proposing that Reg proteins exhibit dimorphic effects. Results: The activity of Reg proteins is not as simplistic as previously perceived but shows auto-immunogenicity depending on different pathophysiological microenvironments. The immunogenicity of Reg proteins could recruit immune cells leading to an anti-tumor effect. Such functional diversity is correlated with their structural characteristics: the N-terminal region contributes to autoantigenicity, while the C-type lectin fragment near the C-terminal determines the trophic action. It should be noted that B-cell masking antigens might also reside within the C-type lectin domain. Conclusions: Reg proteins have dual functional roles under various physiological and pathological conditions. These theoretical foundations facilitate the subsequent development of diagnostic reagents and therapeutic drugs targeting Reg proteins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".