Analysis of long noncoding RNAs and messenger RNAs expression profiles in the hearts of mice with acute viral myocarditis
Bibliographic record
Abstract
Acute viral myocarditis (AVMC) is a common acute myocardial inflammation caused by viral infections, which can lead to severe cardiac dysfunction. Several long noncoding RNAs (lncRNAs) with aberrant expression have been identified in the pathogenesis of AVMC. However, the expression profiles and functions of lncRNAs in AVMC have not been fully elucidated. In the present study, we constructed AVMC mouse models by intraperitoneal injection of coxsackievirus B3 (CVB3) and performed RNA sequencing (RNA-seq) on heart tissues to investigate the differences in lncRNAs and messenger RNAs (mRNAs) expression profiles. Based on the cutoff criteria of adjusted p-values (padj) <0.05 and |log2FoldChange| >1, a total of 1122 differentially expressed lncRNAs (DElncRNAs) and 3186 differentially expressed mRNAs (DEmRNAs) were screened, including 734 upregulated and 388 downregulated lncRNAs, 1821 upregulated and 1365 downregulated mRNAs. RT-qPCR analysis validated that the expression patterns of 12 randomly selected genes (6 DElncRNAs and 6 DEmRNAs) were highly consistent with those in RNA-seq, proving the reliability of the RNA-seq data. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that differentially expressed genes were mainly involved in metabolic and immune-related processes. Furthermore, co-expression networks between DElncRNAs and DEmRNAs in cytokine-cytokine receptor interaction, MAPK signaling pathway, and PI3K-Akt signaling pathway were constructed to study the molecular interactions of these molecules. Our study, for the first time, reveals the expression profiles of lncRNAs and mRNAs associated with AVMC, which may shed light on the roles of lncRNAs in disease pathogenesis and aid in discovering new therapeutic targets.
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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 | 0.001 | 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) | 0.001 | 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".