Composite Living Fibers for Creating Tissue Constructs Using Textile Techniques
Bibliographic record
Abstract
The fabrication of cell-laden structures with anisotropic mechanical properties while having a precise control over the distribution of different cell types within the constructs is important for many tissue engineering applications. Automated textile technologies for making fabrics allow simultaneous control over the color pattern and directional mechanical properties. The use of textile techniques in tissue engineering, however, demands the presence of cell-laden fibers that can withstand the mechanical stresses during the assembly process. Here, the concept of composite living fibers (CLFs) in which a core of load bearing synthetic polymer is coated by a hydrogel layer containing cells or microparticles is introduced. The core thread is drawn sequentially through reservoirs containing a cell-laden prepolymer and a crosslinking reagent. The thickness of the hydrogel layer increases linearly with to the drawing speed and the prepolymer viscosity. CLFs are fabricated and assembled using regular textile processes including weaving, knitting, braiding, winding, and embroidering, to form cell-laden structures. Cellular viability and metabolic activity are preserved during CLF fabrication and assembly, demonstrating the feasibility of using these processes for engineering functional 3D tissue constructs.
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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) | 0.002 | 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".