Scaffold‐free cartilage tissue engineering with a small population of human nasoseptal chondrocytes
Bibliographic record
Abstract
OBJECTIVE: Cartilage tissue engineering is a promising approach to provide suitable materials for nasal reconstruction; however, it typically requires large numbers of cells. We have previously shown that a small number of chondrocytes cultivated within a continuous flow bioreactor can elicit substantial tissue growth, but translation to human chondrocytes is not trivial. Here, we aimed to demonstrate the application of the bioreactor to generate large-sized tissues from a small population of primary human nasoseptal chondrocytes. STUDY DESIGN: Experimental study. METHODS: Chondrocytes were cultured in the bioreactor using different medium compositions, with varying amounts of serum and with or without growth factors. Resulting engineered tissues were analyzed for physical properties, biochemical composition, tissue microstructure, and protein localization. RESULTS: Bioreactor-cultivated constructs grown with serum and growth factors (basic fibroblast growth factor and transforming growth factor beta 2) had greater thickness, as well as DNA and glycosaminoglycan (GAG) contents, compared to low serum and no growth factor controls. These constructs also showed the most intense proteoglycan and collagen II staining. CONCLUSION: The combination of bioreactor conditions, serum, and growth factors allowed the generation of large, thick scaffold-free human cartilaginous tissues that resembled the native nasoseptal cartilage. There also may be implications for patient selection in future clinical applications of these engineered tissues because their GAG content decreased with donor age. LEVEL OF EVIDENCE: NA. Laryngoscope, 127:E91-E99, 2017.
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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) | 0.000 | 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) | 0.002 | 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".