Heart‐On‐a‐Chip with Integrated Ultrasoft Mechanosensors for Continuous Measurement of Cell‐ and Tissue‐Scale Contractile Stresses
Bibliographic record
Abstract
Heart-on-a-chip platforms aim to recapitulate cardiac tissue structure and function in vitro. Traditionally, microfabricated pillars are used to estimate contractile forces based on pillar deflection. However, this approach measures only global forces at the pillar interface and lacks the spatial resolution needed to capture local mechanical stresses. In this study, we present a non-destructive optical method for continuous, multi-scale stress mapping using ultrasoft edge-labeled micro-spherical stress gauges (eMSGs). These embedded mechanosensors visibly deform in response to cellular and extracellular matrix (ECM)-generated stresses, enabling real-time measurements at cell and tissue scales. Our platform features dual cell-seeding chambers with flexible polydimethylsiloxane pillars, into which neonatal rat cardiomyocytes are seeded within a fibrin/Geltrex hydrogel containing eMSGs. Over time, tissues compacted, aligned, and exhibited spontaneous contractions and calcium transients. By modulating ECM composition, we found that reduced fibrin concentration enhanced contractile frequency, regularity, and force generation. Analysis of eMSG deformation enabled calculation of lateral and longitudinal stresses, revealing the impact of compaction and contraction on local mechanics. Finally, drug testing was performed using norepinephrine, which enhanced contractile force, and blebbistatin, which inhibited contraction, demonstrating robust pharmacological responsiveness. This platform provides a powerful tool for real-time biomechanical analysis and drug testing in engineered cardiac tissues.
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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 | 0.000 | 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".