Embedded 3D Bioprinting of Gelatin Methacryloyl-Based Constructs with Highly Tunable Structural Fidelity
Bibliographic record
Abstract
Three-dimensional (3D) bioprinting of hydrogel-based constructs at adequate consistency and reproducibility can be obtained through a compromise between the hydrogel's inherent instability and printing fidelity. There is an increasing demand to develop bioprinting modalities that enable high-fidelity fabrication of 3D hydrogel structures that closely correspond to the envisioned design. In this work, we performed a systematic, in-depth characterization and optimization of embedded 3D bioprinting to create 3D gelatin-methacryloyl (gelMA) structures with highly controlled fidelity using Carbopol as suspension bath. The role of various embedded printing process parameters in bioprinting fidelity was investigated using a combination of experimental and theoretical approaches. We examined the effect of rheological properties of gelMA and Carbopol at varying concentrations, as well as printing conditions on the volumetric flow rate of gelMA bioink. Printing speed was examined and optimized to successfully print gelMA into the support bath at varying Carbopol concentrations. Printing fidelity was characterized in terms of printed strand diameter, uniformity, angle, and area. The optimal Carbopol solution that retained filament shape at highest fidelity was determined. The efficacy of developed bioprinting approach was then demonstrated by fabricating 3D hydrogel constructs with varying geometries and visualized using an advanced synchrotron-based imaging technique. We also investigated the influence of the Carbopol medium on cross-linking and the resulting stiffness of gelMA constructs. Finally, in vitro cytotoxicity of the developed bioprinting approach was assessed by printing human umbilical vein endothelial cells encapsulated in the gelMA bioink. These results demonstrate the significance of the close interplay between bioink-support bath rheology and printing parameters and help to establish an optimized workflow for creating 3D hydrogel structures with high fidelity and cytocompatibility via embedded bioprinting techniques. This robust platform could further expand the application of bioprinted soft tissue constructs in a wide variety of biomedical applications.
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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".