Morphodynamic Tissues via Integrated Programmable Shape Memory Actuators
Bibliographic record
Abstract
Abstract Manipulating the shape of preformed living tissues can present a novel fabrication route toward complex biological architectures. However, external manipulation of tissues can be challenging to implement robustly at multiple length scales and with high degrees of freedom, particularly in soft fibrous tissue constructs. Here, a versatile platform is developed to drive soft tissue morphodynamics using embeddable shape memory actuators that generate multiscale, repetitive, and highly customized tissue deformation on demand. To achieve this, a thermally isolating coating technique is designed and developed for programmable shape memory wires, which protects surrounding biological materials from cytotoxic heating effects during wire actuation. The coated tissue actuators (CTAs) can then be embedded in engineered tissues and activated to produce both large‐ and small‐scale tissue deformations in a highly customized and reproducible manner. Using this strategy, tissues can be forced to adopt specified shapes, with precise control over cell elongation and orientation within an encapsulating matrix. Furthermore, the system can produce predictable, highly localized, and customizable strains within fibrous matrices, capable of elongating cells and biasing their orientation within degrees of a desired direction. This strategy may hence have broad applicability in both applied tissue biofabrication and for fundamental studies of cell–matrix interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".