miR133a regulates cardiomyocyte hypertrophy in diabetes
Bibliographic record
Abstract
BACKGROUND: Diabetic cardiomyopathy, characterized by cardiac hypertrophy and contractile dysfunction, eventually leads to heart failure. We have previously shown that alterations of a number of key molecules are involved in producing cardiomyocyte hypertrophy in diabetes. The aim of the present study was to determine whether microRNAs (miRNA) play a role in mediating altered gene expression and structural/functional deficits in the heart in diabetes. METHODS: STZ-induced diabetic mice were haemodynamically investigated after 2 months of diabetes to establish the development of cardiomyopathy. The tissues were then examined for gene expression and microRNA analysis. We further investigated neonatal rat cardiomyocytes to identify the mechanisms of glucose-induced hypertrophy and the potential role of miR133a. RESULTS: Diabetic mice showed myocardial contractile dysfunction and augmented mRNA expression of atrial and brain natriuretic peptides (ANP, BNP), MEF2A and MEF2C, SGK1 and IGF1R compared to age- and sex-matched controls. Cardiac tissues from these mice showed alteration of multiple miRNAs by array analysis including miR133a, which was confirmed by RT-PCR. In vitro exposure of cardiomyocytes to high levels of glucose produced hypertrophic changes and reduced expression of miRNA133a. Finally, transfection of miR133a mimics prevented altered gene expression and hypertrophic changes. CONCLUSION: Data from these studies demonstrate a novel glucose-induced mechanism regulating gene expression and cardiomyocyte hypertrophy in diabetes which is mediated through miR133a.
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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 | 0.000 |
| 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".