Lab-on-a-chip models of cardiac inflammation
Bibliographic record
Abstract
Cardiovascular diseases are the leading cause of morbidity and mortality worldwide with numerous inflammatory cell etiologies associated with impaired cardiac function and heart failure. Inflammatory cardiomyopathy, also known as myocarditis, is an acquired cardiomyopathy characterized by inflammatory cell infiltration into the myocardium with a high risk of progression to deteriorated cardiac function. Recently, amidst the ongoing COVID-19 pandemic, the emergence of acute myocarditis as a complication of SARS-CoV-2 has garnered significant concern. Given its mechanisms remain elusive in conjunction with the recent withdrawal of previously FDA-approved antiviral therapeutics and prophylactics due to unexpected cardiotoxicity, there is a pressing need for human-mimetic platforms to investigate disease pathogenesis, model dysfunctional features, and support pre-clinical drug screening. Traditional in vitro models for studying cardiovascular diseases have inherent limitations in recapitulating the complexity of the in vivo microenvironment. Heart-on-a-chip technologies, combining microfabrication, microfluidics, and tissue engineering techniques, have emerged as a promising approach for modeling inflammatory cardiac diseases like myocarditis. This review outlines the established and emerging conditions of inflamed myocardium, identifying key features essential for recapitulating inflamed myocardial structure and functions in heart-on-a-chip models, highlighting recent advancements, including the integration of anisotropic contractile geometry, cardiomyocyte maturity, electromechanical functions, vascularization, circulating immunity, and patient/sex specificity. Finally, we discuss the limitations and future perspectives necessary for the clinical translation of these advanced technologies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".