Ferrofluid‐Based Bioink for 3D Printed Skeletal Muscle Tissues with Enhanced Force and Magnetic Response
Bibliographic record
Abstract
Abstract 3D printing has emerged as a transformative technology in several manufacturing processes, being of particular interest in biomedical research for allowing the creation of 3D structures that mimic native tissues. The process of tissue 3D printing entails the construction of functional, 3D tissue structures. In this article, the integration of ferrofluid consisting of iron oxide nanoparticles into muscle cell‐laden bioink is presented to obtain a 3D printed magnetically responsive muscle tissue, i.e., the ferromuscle. Using extrusion‐based methods, the seamless integration of biocompatible ferrofluids are achieved to cell‐laden hydrogels. The resulting ferromuscle tissue exhibits improved tissue differentiation demonstrated by the increased force output upon electrical stimulation compared to muscle tissue prepared without ferrofluid. Moreover, the magnetic component originating from the iron oxide nanoparticles allows magnetic guidance, as well as good cytocompatibility and biodegradability in cell culture. These findings offer a new versatile fabrication approach to integrate magnetic components into living constructs, with potential applications as bioactuators and for future integration in smart, functional muscle implants.
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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 | 0.001 | 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".