MicroRNAs as stress regulators in pancreatic beta cells and diabetes
Bibliographic record
Abstract
MicroRNAs have emerged as important regulatory non-coding RNAs that tune cellular responses to physiological perturbations and disease conditions. An increasing body of literature underlines the important roles of miRNA function in pancreatic β-cells in response to metabolic, genetic and inflammatory stress. Lessons from genetic loss- and gain-of-function studies have implicated several highly expressed and evolutionary conserved miRNAs in stress signal modulation, resolution and buffering, thereby forming stabilizing miRNA networks that preserve β-cell differentiation, function, proliferation and cell survival. This review will summarize our current knowledge of how biologically relevant miRNAs regulate stress responses in pancreatic β-cells, discuss the challenges and opportunities associated with using secreted miRNAs as biomarkers and forecast how mechanistic knowledge of miRNA function can be exploited in developing miRNA-based therapeutics. miRNAs play important roles in the function, differentiation, proliferation, and survival of pancreatic β-cells. Many miRNA families that are regulated by metabolic, genetic, and inflammatory stressors have been found to coordinate the adaptive responses of β-cells in vivo in conditions such as obesity and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".