Identification of cardiac long non-coding RNA profile in human dilated cardiomyopathy
Bibliographic record
Abstract
Aims: Dilated cardiomyopathy (DCM) induced chronic heart failure is a life-threatening disease worldwide. Long non-coding RNAs (lncRNAs) are potential new therapeutic targets and may provide new pathophysiological mechanisms for development of DCM. Methods and results: Microarray assays in 14 DCM and 10 control human heart samples identified 313 significantly differentially expressed lncRNAs. SiRNAs were used to explore the potential function of specific lncRNAs (RP11-544D21.2 and XLOC_014288) in human cardiac myocytes, human cardiac fibroblasts and human cardiac microvascular endothelial cells, respectively. RNA-seq and pathway analyses revealed the function of lncRNA RP11-544D21.2 and XLOC_014288 in three heart cells. Furthermore, impaired tube formation and migration were observed in si-RP11-544D21.2-treated endothelial cells. Moreover, TAF10 was predicted and verified to be one of the causes of some up-regulated lncRNAs. Then, we found that the left ventricular ejection fraction correlated with these changed lncRNAs. Conclusions: This study provides a comparison of human cardiac lncRNA profiles in control and DCM. It illustrates the function and complex roles of the differentially expressed lncRNAs in different cell types in the heart, which may lead to new insights into the mechanisms and treatment strategies for DCM in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".