Effect of cell seeding density on matrix‐forming capacity of meniscus fibrochondrocytes and nasal chondrocytes in meniscus tissue engineering
Bibliographic record
Abstract
The presence of intact menisci is imperative for the proper function of the knee joint. Meniscus injuries are often treated by the surgical removal of the damaged tissue, which increases the likelihood of post-traumatic osteoarthritis. Tissue engineering holds great promise in producing viable engineered meniscal tissue for implantation using the patient's own cells; however, the cell source for producing the engineered tissue is unclear. Nasal chondrocytes (NC) possess many attractive features for engineering meniscus. However, in order to validate the use of NC for engineering meniscus fibrocartilage, a thorough comparison of NC and meniscus fibrochondrocytes (MFC) must be considered. Our study presents an analysis of the relative features of NC and MFC and their respective chondrogenic potential in a pellet culture model. We showed considerable differences in the cartilage tissue formed by the two different cell types. Our data showed that NC were more proliferative in culture, deposited more extracellular matrix, and showed higher expression of chondrogenic genes than MFC. Overall, our data suggest that NC produce superior cartilage tissue to MFC in a pellet culture model. In addition, NCs produce higher quality cartilage tissue at higher cell seeding densities during cell expansion.
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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 | 0.001 | 0.001 |
| 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".