<i>In Vitro</i> Cartilage Tissue Formation by Co-culture of Primary and Passaged Chondrocytes
Bibliographic record
Abstract
Passaging chondrocytes to increase cell number is one way to overcome the major limitation to cartilage tissue engineering, which is obtaining sufficient numbers of chondrocytes to form large amounts of tissue. Because neighboring cells can influence cell phenotype and because passaging induces dedifferentiation, we examined whether coculture of primary and passaged bovine articular chondrocytes in 3-dimensional culture would form cartilage tissue in vitro. Chondrocytes passaged in monolayer culture up to 4 times were mixed with primary (nonpassaged) chondrocytes (5-40% of total cell number) and grown on filter inserts for up to 4 weeks. Passaged cells alone did not form cartilage, but with the addition of increasing numbers of primary chondrocytes, up to 20%, there was an increase in cartilage tissue formation as determined histologically and biochemically and demonstrated by increasing proteoglycan and collagen accumulation. The passaged cells appeared to be undergoing redifferentiation, as indicated by up-regulation of aggrecan, type II collagen, and SOX9 gene expression and decreased type I collagen expression. This switch in collagen type was confirmed using Western blots. Confocal microscopy showed that fluorescently labeled primary cells were distributed throughout the tissue. This coculture approach could provide a new way to solve the problem of limited cell number 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".