Development of Scaffold-Free Elastic Cartilaginous Constructs with Structural Similarities to Auricular Cartilage
Bibliographic record
Abstract
External ear reconstruction with autologous cartilage still remains one of the most difficult problems in the fields of plastic and reconstructive surgery. As the absence of tissue vascularization limits the ability to stimulate new tissue growth, relatively few surgical approaches are currently available (alloplastic implants or sculpted autologous cartilage grafts) to repair or reconstruct the auricle (or pinna) as a result of traumatic loss or congenital absence (e.g., microtia). Alternatively, tissue engineering can offer the potential to grow autogenous cartilage suitable for implantation. While tissue-engineered auricle cartilage constructs can be created, a substantial number of cells are required to generate sufficient quantities of tissue for reconstruction. Similarly, as routine cell expansion can elicit negative effects on chondrocyte function, we have developed an approach to generate large-sized engineered auricle constructs (≥3 cm(2)) directly from a small population of donor cells (20,000-40,000 cells/construct). Using rabbit donor cells, the developed bioreactor-cultivated constructs adopted structural-like characteristics similar to native auricular cartilage, including the development of distinct cartilaginous and perichondrium-like regions. Both alterations in media composition and seeding density had profound effects on the formation of engineered elastic tissue constructs in terms of cellularity, extracellular matrix accumulation, and tissue structure. Higher seeding densities and media containing sodium bicarbonate produced tissue constructs that were closer to the native tissue in terms of structure and composition. Future studies will be aimed at improving the accumulation of specific tissue constituents and determining the clinical effectiveness of this approach using a reconstructive animal model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".