Development of photoreactive collagen-based bioinks for stereolithography 3D bioprinting
Bibliographic record
Abstract
Owing to its significance as a structural protein and essential component of the extracellular matrix (ECM), collagen has found versatile applications in tissue engineering, particularly in the emerging field of 3D bioprinting techniques. Nevertheless, the limited mechanical strength and poor printability of collagen could impede its application as a bioink. In the present study, the combination of collagen methacrylate (ColMA) and poly(ethylene glycol) diacrylate (PEGDA) was investigated as a photocrosslinkable bioink and carrier for human corneal stromal cell (hCSCs) delivery. In this regard, different concentrations of PEGDA and then 1-vinyl-2-pyrrolidinone (NVP) were optimized based on the mechanical properties of the 3D bioprinted samples and their cytocompatibility to hCSCs. It was observed that cell viability decreased as both PEG (ranging from 5 to 10 wt%) and NVP (ranging from 0.25 to 1 wt%) concentrations increased. While the PEG concentration remained constant at 5 wt%, the NVP concentration was optimized. The effect of NVP concentrations of 0.5 and 1 wt% (as the optimal formulations) on the physical, mechanical, and biological properties of the 3D-printed hydrogels was investigated. Additionally, the influence of scaffold geometry on cell alignment was also observed. In all geometries, cells tended to distribute more at sharp ends and proliferated more within 3D bioprinted samples containing 0.5 wt% NVP. On the other hand, the expressions of collagen type I (Col I) and lumican (Lum) were significantly higher in cells encapsulated within 3D bioprinted samples containing 1 wt% NVP compared to samples containing 0.5 wt% NVP. However, both 3D bioprinted samples had great biological properties. Therefore, depending on the desired impact of the 3D bioprinted samples on cells, both combinations demonstrated appropriate cell viability and growth.
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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) | 0.000 | 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 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".