Generating Mechanically Stable, Pediatric, and Scaffold-Free Nasal Cartilage Constructs <i>In Vitro</i>
Bibliographic record
Abstract
Traditional methods of cartilage tissue engineering rely on the use of scaffolds. Although successful chondrogenesis has been reported in scaffold-based constructs, the use of exogenous materials has limited their application due to eliciting host immunogenic responses and potentially resulting in construct failure. As a result, tissue engineering approaches, which aim to generate scaffold-free cartilaginous constructs, have become of particular interest. Here, we generated stable three-dimensional scaffold-free cartilaginous constructs by cultivating expanded pediatric nasal chondrocyte multilayers in a slow turning lateral vessel bioreactor system under chemically defined media. Bioreactor cultivation resulted in increased construct cellularity, fourfold tissue thickness, and 200% sulfated glycosaminoglycan deposition with respect to static culture equivalent cultures. These improvements led to significantly enhanced mechanical and biochemical properties of bioreactor-cultivated constructs, allowing them to support their own weight, while static culture constructs remained fragile. Consequently, bioreactor-cultivated constructs closely resembled native nasal cartilage tissue histologically, mechanically, and biochemically. We propose that this method of cartilage construct formation could be used to obtain readily available human scaffold-free cartilaginous constructs.
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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 | 0.000 | 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) | 0.001 | 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".