Integrative single-cell transcriptome analysis reveals new insights into post-COVID-19 pulmonary fibrosis and potential therapeutic targets
Bibliographic record
Abstract
Abstract The global COVID-19 pandemic caused by the SARS-CoV-2 virus has resulted in a significant number of patients experiencing persistent symptoms, including post-COVID pulmonary fibrosis (PCPF). This study aimed to identify novel therapeutic targets for PCPF using single-cell RNA-Sequencing data from lung tissues of COVID-19 patients, idiopathic pulmonary fibrosis (IPF) patients, and a rat TGF-β1-induced fibrosis model treated with antifibrotic drugs. Patients with COVID-19 had lower alveolar macrophage counts than healthy controls, whereas patients with COVID-19 and IPF presented with elevated monocyte-derived macrophage counts. A differential gene expression analysis showed that macrophages play a crucial role in IPF and COVID-19 development and progression, and fibrosis- and inflammation-associated genes were upregulated in both conditions. Pathway analysis revealed upregulation of inflammation and proteolysis and downregulation of ribosome biogenesis and respiratory gas exchange. Cholesterol efflux and glycolysis were augmented in both macrophage types. The study suggests that antifibrotic drugs may reverse critical lung fibrosis mediators in COVID-19. The results help clarify the molecular mechanisms underlying pulmonary fibrosis in patients with severe COVID-19 and IPF and highlight the potential efficacy of antifibrotic drugs in COVID-19 therapy. Thus, the study's results may have significant implications for the development of new treatment strategies for PCPF.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".