Programmable Melt Electrowriting to Engineer Soft Connective Tissues with Prescribed, Biomimetic, Biaxial Mechanical Properties
Bibliographic record
Abstract
Abstract Appropriate load‐bearing function of soft connective tissues is provided by their nonlinear and often anisotropic mechanics. Recapitulating such complex mechanical behavior in tissue‐engineered structures is particularly crucial, as deviation from native tissue mechanics can trigger pathological biomechanical pathways, causing adverse tissue remodeling and dysfunction. Here, a novel method combining computational modeling, melt electrowriting (MEW), and design of experiments (DOE) is reported to generate scaffolds composed of sinusoidal fibers with prescribed biaxial mechanical properties, recapitulating the distinct nonlinear, anisotropic stress–strain behavior of three model tissues: adult aortic valve, pediatric pulmonary valve, and pediatric pericardium. Finite element analysis is used to efficiently optimize scaffold architecture over a broad parameter space, representing up to 65 conditions, to define MEW print parameters to achieve polycaprolactone scaffolds with target mechanical properties. Architectural parameters are further optimized experimentally using DOE and regression to account for uncertainties involved in the simulation, yielding functional scaffolds with accurate, prescribed mechanics. The prescribed architecture also primarily governs the mechanics of hybrid structures generated by casting cell‐laden fibrin hydrogel within the scaffolds. This high‐fidelity approach recapitulates biaxial mechanical properties over a broad range of mechanical nonlinearity and anisotropy and is generalizable for programmed biofabrication in a variety of tissue engineering applications.
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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".