Templated Assembly of Collagen Fibers Directs Cell Growth in 2D and 3D
Bibliographic record
Abstract
Collagen is widely used in tissue engineering and regenerative medicine, with many examples of collagen-based biomaterials emerging in recent years. While there are numerous methods available for forming collagen scaffolds from isolated collagen, existing biomaterial processing techniques are unable to efficiently align collagen at the microstructural level, which is important for providing appropriate cell recognition and mechanical properties. Although some attention has shifted to development of fiber-based collagen biomaterials, existing techniques for producing and aligning collagen fibers are not appropriate for large-scale fiber manufacturing. Here, we report a novel biomaterial fabrication approach capable of efficiently generating collagen fibers of appropriate sizes using a viscous solution of dextran as a dissolvable template. We demonstrate that myoblasts readily attach and align along 2D collagen fiber networks created by this process. Furthermore, encapsulation of collagen fibers with myoblasts into non-cell-adherent hydrogels promotes aligned growth of cells and supports their differentiation. The ease-of-production and versatility of this technique will support future development of advanced in vitro tissue models and materials for regenerative medicine.
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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 | 0.001 |
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".