3D Printing of Porous Cell-Laden Hydrogel Constructs for Potential Applications in Cartilage Tissue Engineering
Bibliographic record
Abstract
Hydrogels are particularly attractive as scaffolding materials for cartilage tissue engineering because their high water content closely mimics the native extracellular matrix (ECM). Hydrogels can also provide a three-dimensional (3D) microenvironment for homogeneously suspended cells that retains their rounded morphology and thus facilitates chondrogenesis in cartilage tissue engineering. However, fabricating hydrogel scaffolds or cell-laden hydrogel constructs with a predesigned external shape and internal structure that does not collapse remains challenging because of the low viscosity and high water content of hydrogel precursors. Here, we present a study on the fabrication of (cell-laden) alginate hydrogel constructs using a 3D bioplotting system supplemented with a submerged cross-linking process. Swelling, mechanical properties and protein release profiles were examined and tuned by controlling the initial cross-linking density. Porous cell-laden alginate hydrogel constructs were also fabricated and cell viability, cell proliferation, and cartilaginous ECM deposition were investigated. The fabrication technique and the hydrogel scaffolds obtained supported high cell viability and the deposition of cartilaginous ECM, demonstrating their potential for applications in the field of cartilage tissue engineering.
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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".