Development of a hyaluronic acid—collagen bioink for shear-induced fibers and cells alignment
Bibliographic record
Abstract
, the ability to closely mimic this complexity is limited. However, in the last years, extrusion bioprinting showed a certain potential for aligning cells and biomolecules, due to the application of shear stress during the bio-fabrication process. In this work, we propose a strategy to combine collagen (col) with tyramine-modified hyaluronic acid (THA) to obtain a printable col-THA bioink for extrusion bioprinting, solely-based on natural-derived components. Collagen fibers formation within the hybrid hydrogel, as well as collagen distribution and spatial organization before and after printing, were studied. For the validation of the biological outcome, fibroblasts were selected as cellular model and embedded in the col-THA matrix. Cell metabolic activity and cell viability, as well as cell distribution and alignment, were studied in the bioink before and after bioprinting. Results demonstrated successful collagen fibers formation within the bioink, as well as collagen anisotropic alignment along the printing direction. Furthermore, results revealed suitable biological properties, with a slightly reduced metabolic activity at day 1, fully recovered within the first 3 d post-cell embedding. Finally, results showed fibroblasts elongation and alignment along the bioprinting direction. Altogether, results validated the potential to obtain collagen-based bioprinted constructs, with both cellular and ECM anisotropy, without detrimental effects of the fabrication process on the biological outcome. This bioink can be potentially used for a wide range of applications in tissue engineering and regenerative medicine in which anisotropy is required.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".