Patient-Specific 3D Heart-On-a-Chip Model of Dilated Cardiomyopathy with Embedded Bead-Based Mapping of Tissue Contractility
Bibliographic record
Abstract
Abstract Dilated cardiomyopathy (DCM) is the leading cause of heart transplantation, with a 50% risk of progression to heart failure within five years. Conventional disease modeling approaches fail to recapitulate the sophisticated function of the human heart. Alternatively, heart-on-a-chip (HOC) platforms enable real-time monitoring of disease progression and drug responses using miniaturized engineered heart tissues. Here, we developed a functional HOC model using patient-specific human induced pluripotent stem cells (hiPSCs), reprogrammed from the patients’ blood samples. The chip contains two cell-seeding chambers with flexible silicone pillars to support tissue formation. Healthy and DCM hiPSCs were differentiated into cardiomyocytes, combined with an optimized ratio of human cardiac fibroblasts, encapsulated in a fibrin/Geltrex hydrogel (containing fluorescent beads), and seeded in the device chambers. The tissue gradually compacted and started beating spontaneously. Immunofluorescence assay revealed structural abnormalities in DCM tissues, including reduced cell alignment and elongation. The tissue functional responses (e.g., calcium transients and beating) were investigated after 2 weeks of culture, revealing ventricular tachycardia in DCM tissue and highlighting functional hallmarks of the disease. Finally, the model was validated using a drug with known inotropic and chronotropic effects (i.e., norepinephrine). Our platform demonstrated great potential in drug screening, disease modeling, and personalized medicine.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".