Drug-induced regeneration of pancreatic beta cells: An approach to cellular therapeutic targets
Bibliographic record
Abstract
Diabetes mellitus is a common and serious metabolic disease globally, characterized by increased blood glucose levels. The major pathogenesis is the functional impairment of insulin-producing beta cells in the pancreas and the lack of insulin secretion. Although both type 1 and type 2 diabetes develop through distinct pathological mechanisms, they lead to the destruction and/or dysfunction of beta cells, resulting in inadequate beta cell mass to maintain normal blood glucose levels. For this reason, therapeutic agents capable of inducing beta cell proliferation can be considered a possible approach to restore beta cell abundance and treat type 1 and type 2 diabetes. Although several methods have been found to promote the replication of beta cells in animal models or cell lines, it is still challenging to promote the effective proliferation of beta cells in humans. This review highlights the different agents and mechanisms that facilitate pancreatic beta cell regeneration. Numerous small molecules have been discovered to influence beta cell proliferation, primarily by targeting cellular pathways such as DYRK1A, adenosine kinase, SIK, and glucokinase. Additionally, receptors for TGF-β, EGF, insulin, glucagon, GLP-1, SGLT2 inhibitors, and prolactin play critical roles in this process. Stem cell-based clinical trials are also underway to assess the safety and efficacy of stem cell therapies for patients with type 1 and type 2 diabetes. We have emphasized alternative therapeutic pathways and related strategies that may be employed to promote the regeneration of pancreatic beta cells. The knowledge raised within this review may help to understand the potential drug-inducible targets for beta cell regeneration and pave the way for further investigations.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".