Circulating miR-375 as a Biomarker of β-Cell Death and Diabetes in Mice
Bibliographic record
Abstract
Type 1 diabetes is a progressive autoimmune disease that is largely silent in its initial stages. Yet, sensitive methods for detection of β-cell death and prediction and prevention of diabetes are lacking. Micro-RNAs (miRNAs) have been found at high concentrations in body fluids. Here in this study we sought to determine whether an islet enriched miRNA, miR-375, is a suitable blood marker to detect β-cell death and predict diabetes in mice. We measured miR-375 levels by quantitative RT-PCR in plasma samples of streptozotocin (STZ)-treated C57BL/6 mice and nonobese diabetic (NOD) mice. We also measured miR-375 levels in media samples of cytokine- or STZ-treated islets in the presence or absence of cell-death inhibitors. High-dose STZ administration dramatically increased circulating miR-375 levels, prior to the onset of hyperglycemia. Similarly, in the NOD mouse model of autoimmune diabetes, circulating miR-375 levels were significantly increased 2 weeks before diabetes onset. Moreover, cytokine- and STZ-induced cell death in isolated mouse islets produced a striking increase in extracellular miR-375 levels, which was reduced by cell death inhibitors. These data suggest that circulating miR-375 can be used as a marker of β-cell death and potential predictor of 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".