MicroRNAs as stress regulators in pancreatic beta cells and diabetes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it