Decellularized lucky bamboo scaffolds for cartilage tissue engineering
Bibliographic record
Abstract
Abstract Cartilage is a load-bearing connective tissue with limited self-healing capacity and tissue engineering approaches aim to develop functional scaffolds for the repair and regeneration of damaged cartilage. Scaffold porosity and mechanical characteristics play important roles to support cell growth and provide tissue function. In most cases, however, they are inversely correlated. Therefore, manufacturing highly porous scaffolds with suitable mechanical properties is one of the major challenges in cartilage tissue engineering. In this study, lucky bamboo (Dracaena sanderiana) was chosen as a cartilage tissue engineering scaffold since it can provide high porosity (86 ± 10%), appropriate pore size (26 ± 8 µm) and desirable elastic modulus (0.9 ± 0.4 MPa) comparable with native articular cartilage (∼1 MPa). Chemical decellularization was accomplished using sodium dodecyl sulfate to remove the cellular content (−77%) without causing any significant damage to the cellulose structure of the lucky bamboo scaffolds. Decellularized scaffolds were seeded with primary bovine chondrocytes and cultured for up to 8 weeks. Effect on cell proliferation and extracellular matrix (ECM) accumulation were analyzed using biochemical, histological and immunohistochemical methods. A homogenous cell distribution throughout the decellularized scaffolds was observed and the presence of type Ⅱ collagen and aggrecan indicated that the seeded cells retained their chondrogenic phenotype during the culture period. In addition, cellularity and ECM accumulation within the scaffolds significantly increased with time in culture. Overall, these findings were very promising and support decellularized lucky bamboo as a potential scaffold material in cartilage tissue engineering 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) | 0.001 | 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".