Printing Structurally Anisotropic Biocompatible Fibrillar Hydrogel for Guided Cell Alignment
Bibliographic record
Abstract
Many fibrous biological tissues exhibit structural anisotropy due to the alignment of fibers in the extracellular matrix. To study the impact of such anisotropy on cell proliferation, orientation, and mobility, it is important to recapitulate and achieve control over the structure of man-made hydrogel scaffolds for cell culture. Here, we report a chemically crosslinked fibrous hydrogel due to the reaction between aldehyde-modified cellulose nanofibers and gelatin. We explored two ways to induce structural anisotropy in this gel by extruding the hydrogel precursor through two different printheads. The cellulose nanofibers in the hydrogel ink underwent shear-induced alignment during extrusion and retained it in the chemically crosslinked hydrogel. The degree of anisotropy was controlled by the ink composition and extrusion flow rate. The structural anisotropy of the hydrogel extruded through a nozzle affected the orientation of human dermal fibroblasts that were either seeded on the hydrogel surface or encapsulated in the extruded hydrogel. The reported straightforward approach to constructing fibrillar hydrogel scaffolds with structural anisotropy can be used in studies of the biological impact of tissue anisotropy.
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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 | 0.000 |
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".