Role of miR-A in COVID-19: Implications for lung injury and fibrosis
Bibliographic record
Abstract
Introduction: Our research group previously identified the role of miR-A in virus-induced lung injury. However, the role of miR-A in COVID-19-associated lung injury remain elusive. Aims: To evaluate the expression of miR-A in COVID-19 viral infection. Methods: Retrospective tissue samples from 47 COVID-19 autopsies and 6 non-neoplastic lung cancer controls were analyzed for clinical and histological features. RNA-Seq and bioinformatic analysis were conducted, and microRNA expression was assessed via digital droplet polymerase chain reaction (ddPCR). Results: Histological analysis in COVID-19 showed heterogeneous changes, such as septal thickening and hyaline membranes, while controls had preserved parenchyma. ddPCR for miR-A showed increased expression levels (p<0.001). Of 361 miR-A target genes, 59 were differentially expressed (padj<0.05) in COVID (34 up, 25 down). Functional enrichment analysis revealed miR-A-dependent genes were predicted to be involved in epithelial-mesenchymal transition, extracellular matrix organization, and collagen formation. Increased miR-A and enhanced expression of collagen and fibrosis related genes were validated using the humanized SARS-Co-V2 infection model. Conclusions: miR-A is likely to play a key role in the development of fibrosis in the context of COVID-19 viral infection. Further studies are needed to explore the potential of its inhibition in preventing fibrosis progression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".