Bibliographic record
Abstract
Type two diabetes (T2D) is a challenging metabolic disorder for which a cure has not yet been found. Its etiology is associated with several phenomena, including significant loss of insulin-producing, beta cell (β cell) mass via progressive programmed cell death and disrupted cellular autophagy. In diabetes, the etiology of β cell death and the role of mitochondria are complex and involve several layers of mechanisms. Understanding the dynamics of those mechanisms could permit researchers to develop an intervention for the progressive loss of β cells. Currently, diabetes research has shifted toward rejuvenation and plasticity technology and away from the simplified approach of hormonal compensation. Diabetes research is currently challenged by questions such as how to enhance cell survival, decrease apoptosis and replenish β cell mass in diabetic patients. In this review, we discuss evidence that β cell development and mass formation are guided by specific signaling systems, particularly hormones, transcription factors, and growth factors, all of which could be manipulated to enhance mass growth. There is also strong evidence that β cells are dynamically active cells, which, under specific conditions such as obesity, can increase in size and subsequently increase insulin secretion. In certain cases of aggressive or advanced forms of T2D, β cells become markedly impaired, and the only alternatives for maintaining glucose homeostasis are through partial or complete cell grafting (the Edmonton protocol). In these cases, the harvesting of an enriched population of viable β cells is required for transplantation. This task necessitates a deep understanding of the pharmacological agents that affect β cell survival, mass, and function. The aim of this review is to initiate discussion about the important signals in pancreatic β cell development and mass formation and to highlight the process by which cell death occurs in diabetes. This review also examines the attempts that have been made to recover or increase cell mass in diabetic patients by using various pharmacological agents.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".