Modeling Heart Diseases on a Chip: Advantages and Future Opportunities
Bibliographic record
Abstract
Heart disease is a significant burden on global health care systems and is a leading cause of death each year. To improve our understanding of heart disease, high quality disease models are needed. These will facilitate the discovery and development of new treatments for heart disease. Traditionally, researchers have relied on 2D monolayer systems or animal models of heart disease to elucidate pathophysiology and drug responses. Heart-on-a-chip (HOC) technology is an emerging field where cardiomyocytes among other cell types in the heart can be used to generate functional, beating cardiac microtissues that recapitulate many features of the human heart. HOC models are showing great promise as disease modeling platforms and are poised to serve as important tools in the drug development pipeline. By leveraging advances in human pluripotent stem cell-derived cardiomyocyte biology and microfabrication technology, diseased HOCs are highly tuneable and can be generated via different approaches such as: using cells with defined genetic backgrounds (patient-derived cells), adding small molecules, modifying the cells' environment, altering cell ratio/composition of microtissues, among others. HOCs have been used to faithfully model aspects of arrhythmia, fibrosis, infection, cardiomyopathies, and ischemia, to name a few. In this review, we highlight recent advances in disease modeling using HOC systems, describing instances where these models outperformed other models in terms of reproducing disease phenotypes and/or led to drug development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".