In vitro maturation and in vivo stability of bioprinted human nasal cartilage
Bibliographic record
Abstract
The removal of skin cancer lesions on the nose often results in the loss of nasal cartilage. The cartilage loss is either surgically replaced with autologous cartilage or synthetic grafts. However, these replacement options come with donor-site morbidity and resorption issues. 3-dimensional (3D) bioprinting technology offers the opportunity to engineer anatomical-shaped autologous nasal cartilage grafts. The 3D bioprinted cartilage grafts need to embody a mechanically competent extracellular matrix (ECM) to allow for surgical suturing and resistance to contraction during scar tissue formation. We investigated the effect of culture period on ECM formation and mechanical properties of 3D bioprinted constructs of human nasal chondrocytes (hNC)-laden type I collagen hydrogel in vitro and in vivo. Tissue-engineered nasal cartilage constructs developed from hNC culture in clinically approved collagen type I and type III semi-permeable membrane scaffold served as control. The resulting 3D bioprinted engineered nasal cartilage constructs were comparable or better than the controls both in vitro and in vivo. This study demonstrates that 3D bioprinted constructs of engineered nasal cartilage are feasible options in nasal cartilage reconstructive surgeries.
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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".