Toward Hierarchical Assembly of Aligned Cell Sheets into a Conical Cardiac Ventricle Using Microfabricated Elastomers
Bibliographic record
Abstract
Despite current efforts in organ-on-chip engineering to construct miniature cardiac models, they often lack some physiological aspects of the heart, including fiber orientation. This motivates the development of bioartificial left ventricle models that mimic the myofiber orientation of the native ventricle. Herein, an approach relying on microfabricated elastomers that enables hierarchical assembly of 2D aligned cell sheets into a functional conical cardiac ventricle is described. Soft lithography and injection molding techniques are used to fabricate micro-grooves on an elastomeric polymer scaffold with three different orientations ranging from -60° to +60°, each on a separate trapezoidal construct. The width of the micro-grooves is optimized to direct the majority of cells along the groove direction and while periodic breaks are used to promote cell-cell contact. The scaffold is wrapped around a central mandrel to obtain a conical-shaped left ventricle model inspired by the size of a human left ventricle 19 weeks post-gestation. Rectangular micro-scale holes are incorporated to alleviate oxygen diffusional limitations within the 3D scaffold. Cardiomyocytes within the 3D left ventricle constructs showed high viability in all layers after 7 days of cultivation. The hierarchically assembled left ventricle also provided functional readouts such as calcium transients and ejection fraction.
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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) | 0.000 | 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 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".