Extracellular macrostructure anisotropy improves cardiac tissue-like construct function and phenotypic cellular maturation
Bibliographic record
Abstract
Regenerative cardiac tissue is a promising field of study with translational potential as a therapeutic option for myocardial repair after injury, however, poor electrical and contractile function has limited translational utility. Emerging research suggests scaffolds that recapitulate the structure of the native myocardium improve physiological function. Engineered cardiac constructs with anisotropic extracellular architecture demonstrate improved tissue contractility, signaling synchronicity, and cellular organization when compared to constructs with reduced architectural order. The complexity of scaffold fabrication, however, limits isolated variation of individual structural and mechanical characteristics. Thus, the isolated impact of scaffold macroarchitecture on tissue function is poorly understood. Here, we produce isotropic and aligned collagen scaffolds seeded with embryonic stem cell derived cardiomyocytes (hESC-CM) while conserving all confounding physio-mechanical features to independently assess the effects of macroarchitecture on tissue function. We quantified spatiotemporal tissue function through calcium signaling and contractile strain. We further examined intercellular organization and intracellular development. Aligned tissue constructs facilitated improved signaling synchronicity and directional contractility as well as dictated uniform cellular alignment. Cells on aligned constructs also displayed phenotypic and genetic markers of increased maturity. Our results isolate the influence of scaffold macrostructure on tissue function and inform the design of optimized cardiac tissue for regenerative and model medical systems.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".