Adherent agarose mold cultures: An in vitro platform for multi‐factorial assessment of passaged chondrocyte redifferentiation
Bibliographic record
Abstract
Generating the best possible bioengineered cartilage from passaged chondrocytes requires culture condition optimization. In this study, the use of adherent agarose mold (adAM) cultures to support redifferentiation of passaged twice (P2) chondrocytes and serve as a scalable platform to assess the effect of growth factor combinations on proteoglycan accumulation by cells was examined. By 2 days in adAM culture, bovine P2 cells were partially redifferentiated as demonstrated by regression of actin-based dedifferentiation signalling and fibroblast matrix and contractile gene expression. By day 10, aggrecan and type II collagen gene expression were significantly increased in adAM cultured cells. At day 20, a continuous layer of cartilage tissue was observed. There was no evidence of tissue contraction by P2 cells in adAM cultures. The matrix properties of the resultant tissue as well as proteoglycan 4 (PRG4) secreted by the cells were dependent on the initial cell seeding density. AdAM cultures were scalable and culture within small 3 mm diameter adAM allowed for multi-factorial assessment of growth factors on proteoglycan accumulation by human P2 chondrocytes. Although there was a patient specific response in proteoglycan accumulation to the various cocktail combinations, the cocktail consisting of 2 ng/ml TGFβ1, 10 ng/ml FGF2, and 250 ng/ml FGF18 resulted in a consistent increase in alcian blue tissue staining. Additional studies will be required to identify the optimal conditions to bioengineer articular cartilage tissue for clinical use. However, the results to date suggest that adAM cultures may be suitable to use for high throughput assessment. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2392-2405, 2018.
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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) | 0.000 | 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 | 0.001 |
| 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".