Analyzing Biological Performance of 3D-Printed, Cell-Impregnated Hybrid Constructs for Cartilage Tissue Engineering
Bibliographic record
Abstract
Three-dimensional (3D) bioprinting of hybrid constructs is a promising biofabrication method for cartilage tissue engineering because a synthetic polymer framework and cell-impregnated hydrogel provide structural and biological features of cartilage, respectively. During bioprinting, impregnated cells may be subjected to high temperatures (caused by the adjacent melted polymer) and process-induced mechanical forces, potentially compromising cell function. This study addresses these biofabrication issues, evaluating the heat distribution of printed polycaprolactone (PCL) strands and the rheological property and structural stability of alginate hydrogels at various temperatures and concentrations. The biocompatibility of parameters from these studies was tested by culturing 3D hybrid constructs bioprinted with primary cells from embryonic chick cartilage. During initial two-dimensional culture expansion of these primary cells, two morphologically and molecularly distinct cell populations ("rounded" and "fibroblastic") were isolated. The biological performance of each population was evaluated in 3D hybrid constructs separately. The cell viability, proliferation, and cartilage differentiation were observed at high levels in hybrid constructs of both cell populations, confirming the validity of these 3D bioprinting parameters for effective cartilage tissue engineering. Statistically significant performance variations were observed, however, between the rounded and fibroblastic cell populations. Molecular and morphological data support the notion that such performance differences may be attributed to the relative differentiation state of rounded versus fibroblastic cells (i.e., differentiated chondrocytes vs. chondroprogenitors, respectively), which is a relevant issue for cell-based tissue engineering strategies. Taken together, our study demonstrates that bioprinting 3D hybrid constructs of PCL and cell-impregnated alginate hydrogel is a promising approach for 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) | 0.000 | 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".