Emerging role of epigenetic in pulmonary arterial hypertension right ventricular failure
Bibliographic record
Abstract
INTRODUCTION Pulmonary arterial hypertension (PAH) is associated with a global impairment of angiogenesis. Although the origin of the angiogenic defect remains unknown. since a few years epigenetic has become an important research area in many diseases including PAH. Role for miRNAs has been widely demonstrated; nonetheless a role for long noncoding RNA (LncRNA) remains unknown. In the present study we focused our role on the expression level of miR‐126 and MALAT‐1 both of which are enriched in endothelial cells and known to be implicated in angiogenesis regulation. We hypothesized that aberrantly express miR‐126 and MALAT‐1 contributes to angiogenesis impairment see in PAH . RESULTS Using qRT‐PCR we showed that miR126 was decreased in human PAH RV (p<0.05) compare to control while MALAT‐1 expression was upregulated p<0.05). These finding were associated in vitro with a significant reduction in EC proliferation (Ki67;p<0.05) and tube formation (p<0.05) measured in freshly isolated human RV endothelial cells from both PAH compare to EC from control RV. Both miR‐126 and MALAT‐1 are critical regulators of angiogenesis and are likely dependent of each other; in fact we showed that miR‐126 expression inversely correlates with MALAT‐1 expression in PAH RV (p<0.05). CONCLUSION We demonstrated, for the first time in PAH that the endothelial‐enriched noncoding RNAs miR‐126 and MALAT‐1 are aberrantly expressed in human RV from PAH patients contributing to the impairment of angiogenesis. The endothelial specific miR‐126 is known to regulate endothelial cells proliferation while MALAT‐1 up regulation has been associated with decreased endothelial cells migration and sprouting, suggesting that these two noncoding RNAs are implicated in angiogenesis defect.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".