Cardiac miR‐133a overexpression prevents early cardiac fibrosis in diabetes
Bibliographic record
Abstract
Diabetic cardiomyopathy is a cascade of complex events leading to eventual failure of the heart and cardiac fibrosis being considered as one of its major causes. miR-133a is one of the most abundantly expressed microRNAs in the heart. We investigated the role of miR-133a during severe hyperglycaemia. And, our aim was to find out what role miR-133a plays during diabetes-induced cardiac fibrosis. We saw a drastic decrease in miR-133a expression in the hearts of streptozotocin-induced diabetic animals, as measured by RT-qPCR. This decrease was accompanied by an increase in the transcriptional co-activator EP300 mRNA and major markers of fibrosis [transforming growth factor-β1, connective tissue growth factor, fibronectin (FN1) and COL4A1]; in addition, focal cardiac fibrosis assessed by Masson's trichome stain was increased. Interestingly, in diabetic mice with cardiac-specific miR-133aa overexpression, cardiac fibrosis was significantly decreased, as observed by RT-qPCR and immunoblotting of COL4A1, ELISA for FN1 and microscopic examination. Furthermore, Cardiac miR-133a overexpression prevented ERK1/2 and SMAD-2 phosphorylation. These findings show that miR-133a could be a potential therapeutic target for diabetes-induced cardiac fibrosis and related cardiac dysfunction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".