Enabling 3D bioprinting of cell-laden pure collagen scaffolds via tannic acid supporting bath
Bibliographic record
Abstract
The fabrication of cell-laden biomimetic scaffolds represents a pillar of tissue engineering and regenerative medicine (TERM) strategies, and collagen is the gold standard matrix for cells to be. In the recent years, extrusion 3D bioprinting introduced new possibilities to increase collagen scaffold performances thanks to the precision, reproducibility, and spatial control. However, the design of pure collagen bioinks represents a challenge, due to the low storage modulus and the long gelation time, which strongly impede the extrusion of a collagen filament and the retention of the desired shape post-printing. In this study, the tannic acid-mediated crosslinking of the outer layer of collagen is proposed as strategy to enable collagen filament extrusion. For this purpose, a tannic acid solution has been used as supporting bath to act exclusively as external crosslinker during the printing process, while allowing the pH- and temperature-driven formation of collagen fibers within the core. Collagen hydrogels (concentration 2-6 mg/mL) were extruded in tannic acid solutions (concentration 5-20 mg/mL). Results proved that external interaction of collagen with tannic acid during 3D printing enables filament extrusion without affecting the bulk properties of the scaffold. The temporary collagen-tannic acid interaction resulted in the formation of a membrane-like external layer that protected the core, where collagen could freely arrange in fibers. The precision of the printed shapes was affected by both tannic acid concentration and needle diameter and can thus be tuned. Altogether, results shown in this study proved that tannic acid bath enables collagen bioprinting, preserves collagen morphology, and allows the manufacture of a cell-laden pure collagen scaffold.
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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".