SARS-CoV-2 pathogenesis in an angiotensin II–induced heart-on-a-chip disease model and extracellular vesicle screening
Bibliographic record
Abstract
Adverse cardiac outcomes in COVID-19 patients, particularly those with preexisting cardiac disease, motivate the development of human cell-based organ-on-a-chip models to recapitulate cardiac injury and dysfunction and for screening of cardioprotective therapeutics. Here, we developed a heart-on-a-chip model to study the pathogenesis of SARS-CoV-2 in healthy myocardium established from human induced pluripotent stem cell (iPSC)-derived cardiomyocytes and a cardiac dysfunction model, mimicking aspects of preexisting hypertensive disease induced by angiotensin II (Ang II). We recapitulated cytopathic features of SARS-CoV-2-induced cardiac damage, including progressively impaired contractile function and calcium handling, apoptosis, and sarcomere disarray. SARS-CoV-2 presence in Ang II-treated hearts-on-a-chip decreased contractile force with earlier onset of contractile dysfunction and profoundly enhanced inflammatory cytokines compared to SARS-CoV-2 alone. Toward the development of potential therapeutics, we evaluated the cardioprotective effects of extracellular vesicles (EVs) from human iPSC which alleviated the impairment of contractile force, decreased apoptosis, reduced the disruption of sarcomeric proteins, and enhanced beta-oxidation gene expression. Viral load was not affected by either Ang II or EV treatment. We identified MicroRNAs miR-20a-5p and miR-19a-3p as potential mediators of cardioprotective effects of these EVs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".