Improvement of cell deposition by self-absorbent capability of freeze-dried 3D-bioprinted scaffolds derived from cellulose material-alginate hydrogels
Bibliographic record
Abstract
Cell-laden printing is the most commonly used approach in 3D bioprinting. One of the major drawbacks of cell-laden printing is that cell viability is highly affected by the extrusion pressure and shear force in the printing process. We present a new cell-deposition method by using the superabsorbent capability of 3D printed scaffolds with four ink formations: 20:10 nanocrystal/alginate (NCA 20/10), 20:10 nanofiber/alginate (NFA 20/10), 20:02 nanocrystal/alginate (NCA 20/02) and 20:02 nanofiber/alginate (NFA 20/02). Limited pores were observed from the surface of inherent NCA and NFA scaffolds, which may limit the numbers of cells to enter into the scaffolds. Therefore, we designed a dual-porous (DP) structure to connect the inherent pores (IPs) to the scaffold surface. Due to these porous structures, NCA and NFA scaffolds exhibit an excellent capability to absorb cell suspension, which may be used for depositing cells to 3D-printed scaffolds, namely self-absorbent (SA) deposition. Compared to the conventional top-loading (TL) method, the SA method had more uniform cell distributions in the entire 3D-printed scaffolds and higher efficiency of cell deposition. For the TL method, DP scaffold exhibited a more uniform cell distribution, which may provide a better microenvironment for the cells in comparison to the IP scaffold. For both cell loading methods, a rapid increase of cell number was observed in the first 4 days of culture in the 3D-printed NCA and NFA structures. NFA 20/02 exhibits the best cell viability compared to the other three inks. In conclusion, the SA method may serve as a new approach for loading cells in cell-free 3D-bioprinting, and DP design could improve the efficiency of the cell deposition.
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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) | 0.001 | 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".