Design and Dynamic Culture of 3D-Scaffolds for Cartilage Tissue Engineering
Bibliographic record
Abstract
Engineered scaffolds for tissue-engineering should be designed to match the stiffness and strength of healthy tissues while maintaining an interconnected pore network and a reasonable porosity. In this work, we have used 3D-plotting technique to produce poly-L-Lactide macroporous scaffolds with two different pore sizes. The ability of these macroporous scaffolds to support chondrocyte attachment and viability were compared under static and dynamic loading in vitro. Moreover, the 3D-plotting technique was combined with porogen-leaching, leading to macro/microporous scaffolds, so as to examine the effect of microporosity on the level of cell attachment and viability under similar loading condition. Canine chondrocytes' cells were seeded onto the scaffolds with different topologies, and the constructs were cultured for up to 2 weeks under static conditions or in a bioreactor under dynamic compressive strain of 10% strain, at a frequency of 1 Hz. The attachment and cell growth of chondrocytes were examined by scanning electron microscopy and by 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. A significant difference in cell attachment was observed in macroporous scaffolds with different pore sizes after 1, 7, and 14 days. Cell viability in the scaffolds was enhanced with decreasing pore size and increasing microporosity level throughout the culture period. Chondrocyte viability in the scaffolds cultured under dynamic loading was significantly higher (p<0.05) than the scaffolds cultured statically. Dynamic cell culture of the scaffolds improved cell viability and decreased the time of in vitro culture when compared to statically cultured constructs. Optimizing the culture conditions and scaffold properties could generate optimal tissue/constructs combination for cartilage repair.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".