Templated Assembly of Collagen Fibers Directs Cell Growth in 2D and 3D
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it