MiR-150-5p retards the progression of myocardial fibrosis by targeting <i>EGR1</i>
Bibliographic record
Abstract
To investigate the differential expression of microRNA-150-5p (miR-150-5p) and early growth response 1 (EGR1) in myocardial fibrosis (MF) cells, and determine the effect between miR-150-5p and EGR1 on MF. Human MF cells were generated via Trypanosoma cruzi (T. cruzi) infection, a mouse model of MF was generated via angiotensin II. The expression levels of miR-150-5p and EGR1 were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and western blot assay. The correlation between miR-150-5p and EGR1 was confirmed by a luciferase reporter assay. The viability, proliferation, and apoptotic rate were detected by cell counting kit-8 (CCK-8), colony-formation and flow cytometry assays. Hematoxylin-eosin (HE) staining and Masson staining visualized the degree of MF. Echocardiography was performed to obtain the levels of left ventricle fractional shortening (LVFS) and left ventricle ejection fraction (LVEF), computer algorithms and a videographics program were used to obtain the levels of left ventricular systolic pressure (LVSP), left ventricular end diastolic pressure (LVEDP) and ±left ventricular dp/dt maximum (LV dp/dtmax). We found that the expression of miR-150-5p in MF cells was lower than normal cardiomyocytes, while the expression level of EGR1 in MF cells were higher than normal cardiomyocytes. Cell experiments demonstrated that EGR1 and miR-150-5p could influence the development of MF, and the expression of EGR1 in cardiomyocytes was regulated by miR-150-5p directly. Lastly, we confirmed that sh-Egr1 would decrease the severity of MF, while miR-150-5p antagomir could aggravate MF. Our results illustrate the mechanism of MF development, and provide a potential target for MF treatment.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".