Development of large engineered cartilage constructs from a small population of cells
Bibliographic record
Abstract
Confronted with articular cartilage's limited capacity for self-repair, joint resurfacing techniques offer an attractive treatment for damaged or diseased tissue. Although tissue engineered cartilage constructs can be created, a substantial number of cells are required to generate sufficient quantities of tissue for the repair of large defects. As routine cell expansion methods tend to elicit negative effects on chondrocyte function, we have developed an approach to generate phenotypically stable, large-sized engineered constructs (≥3 cm(2) ) directly from a small amount of donor tissue or cells (as little as 20,000 cells to generate a 3 cm(2) tissue construct). Using rabbit donor tissue, the bioreactor-cultivated constructs were hyaline-like in appearance and possessed a biochemical composition similar to native articular cartilage. Longer bioreactor cultivation times resulted in increased matrix deposition and improved mechanical properties determined over a 4 week period. Additionally, as the anatomy of the joint will need to be taken in account to effectively resurface large affected areas, we have also explored the possibility of generating constructs matched to the shape and surface geometry of a defect site through the use of rapid-prototyped defect tissue culture molds. Similar hyaline-like tissue constructs were developed that also possessed a high degree of shape correlation to the original defect mold. Future studies will be aimed at determining the effectiveness of this approach to the repair of cartilage defects in an animal model and the creation of large-sized osteochondral constructs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".