Molecular Basis of Cardiac and Vascular Injuries Associated With COVID-19
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) is a viral respiratory illness caused by the novel coronavirus SARS-CoV-2. The presence of the pre-existing cardiac disease is associated with an increased likelihood of severe clinical course and mortality in patients with COVID-19. Besides, current evidence indicates that a significant number of patients with COVID-19 also exhibit cardiovascular involvement even in the absence of known cardiac risk factors. Therefore, there is a need to understand the underlying mechanisms and genetic predispositions that explain cardiovascular involvement in COVID-19. Objectives In silico analysis of publicly available datasets to decipher the molecular basis, potential pathways, and the role of the endothelium in the pathogenesis of cardiac and vascular injuries in COVID-19. Materials and Methods Consistent significant differentially expressed genes (DEGs) shared by endothelium and peripheral immune cells were identified in five microarray transcriptomic profiling datasets in patients with venous thromboembolism “VTE,” acute coronary syndrome, heart failure and /or cardiogenic shock (main cardiovascular injuries related to COVID-19) compared to healthy controls. The identified genes were further examined in the publicly available transcriptomic dataset for cell/tissue specificity in lung tissue, in different ethnicities and in SARS-CoV-2 infected versus mock-infected lung tissues and cardiomyocytes. Results We identified 36 DEGs in blood and endothelium known to play key roles in endothelium and vascular biology, regulation of cellular response to stress as well as endothelial cell migration. Some of these genes were upregulated significantly in SARS-CoV-2 infected lung tissues. On the other hand, some genes with cardioprotective functions were downregulated in SARS-CoV-2 infected cardiomyocytes. Conclusion In conclusion, our findings from the analysis of publicly available transcriptomic datasets identified shared core genes pertinent to cardiac and vascular-related injuries and their probable role in genetic susceptibility to cardiovascular injury in patients with COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".