Engineering of scaffold‐free tri‐layered auricular tissues for external ear reconstruction
Bibliographic record
Abstract
OBJECTIVES: Current strategies for external ear reconstruction can lead to donor site morbidity and/or surgical complications. Tissue-engineered auricular tissues may provide readily available reconstructive materials that resemble native auricular tissue, which is composed of a cartilaginous region sandwiched between two perichondrial layers. We previously developed scaffold-free bi-layered auricular tissues, consisting of a perichondrial layer and a cartilaginous layer, by cultivating chondrocytes and perichondrial cells in a continuous flow bioreactor. Here, we aimed to improve construct properties and develop strategies to engineer tri-layered auricular constructs that better mimic native auricular tissue. STUDY DESIGN: Experimental study. METHODS: Different concentrations of insulin-like growth factor (IGF)-1 and insulin were supplemented during bioreactor culture to determine conditions for engineering bi-layered constructs. We also investigated two methods of engineering tri-layered constructs. Method 1 used Ficoll separation to isolate perichondrial cells, followed by the seeding of isolated perichondrial cells onto the opposing side of the bi-layered constructs. Method 2 involved the growth of the bi-layered constructs in osteogenic culture medium. RESULTS: The combination of 10 nM IGF-1 and 100 nM insulin led to increased collagen content in the engineered bi-layered constructs. For developing tri-layered constructs, method 2 yielded thicker constructs with better mechanical and biochemical properties compared to method 1. In addition, the presence of the perichondrial layers protected the engineered constructs from tissue calcification. CONCLUSION: Auricular tissues with a biomimetic microstructure can be created by growing chondrocytes and perichondrial cells in a continuous flow bioreactor, followed by cultivation in osteogenic medium. LEVEL OF EVIDENCE: NA Laryngoscope, 129:E272-E283, 2019.
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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 | 0.001 | 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".