Heart-on-a-chip model of immune-induced cardiac dysfunction reveals the role of free mitochondrial DNA and therapeutic effects of endothelial exosomes
Bibliographic record
Abstract
Abstract Cardiovascular disease continues to take more human lives than all cancer combined, prompting the need for improved research models and treatment options. Despite a significant progress in development of mature heart-on-a-chip models of fibrosis and cardiomyopathies starting from induced pluripotent stem cells (iPSCs), human cell-based models of myocardial inflammation are lacking. Here, we bioengineered a vascularized heart-on-a-chip system with circulating immune cells to model SARS-CoV-2-induced acute myocarditis. Briefly, we observed hallmarks of COVID-19-induced myocardial inflammation in the heart-on-a-chip model, as the presence of immune cells augmented the expression levels of proinflammatory cytokines, triggered progressive impairment of contractile function and altered intracellular calcium transient activities. An elevation of circulating cell-free mitochondrial DNA (ccf-mtDNA) was measured first in the in vitro heart-on-a-chip model and then validated in COVID-19 patients with low left ventricular ejection fraction (LVEF), demonstrating that mitochondrial damage is an important pathophysiological hallmark of inflammation induced cardiac dysfunction. Leveraging this platform in the context of SARS-CoV-2 induced myocardial inflammation, we established that administration of human umbilical vein-derived EVs effectively rescued the contractile deficit, normalized intracellular calcium handling, elevated the contraction force and reduced the ccf- mtDNA and chemokine release via TLR-NF-kB signaling axis.
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) | 0.000 | 0.000 |
| 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) | 0.002 | 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".