Human-engineered auricular reconstruction (hEAR) by 3D-printed molding with human-derived auricular and costal chondrocytes and adipose-derived mesenchymal stem cells
Bibliographic record
Abstract
Abstract Microtia is a small, malformed external ear, which occurs at an incidence of 1–10 per 10 000 births. Autologous reconstruction using costal cartilage is the most widely accepted surgical microtia repair technique. Yet, the method involves donor-site pain and discomfort and relies on the artistic skill of the surgeon to create an aesthetic ear. This study employed novel tissue engineering techniques to overcome these limitations by developing a clinical-grade, 3D-printed biodegradable auricle scaffold that formed stable, custom-made neocartilage implants. The unique scaffold design combined strategically reinforced areas to maintain the complex topography of the outer ear and micropores to allow cell adhesion for the effective production of stable cartilage. The auricle construct was computed tomography (CT) scan-based composed of a 3D-printed clinical-grade polycaprolactone scaffold loaded with patient‐derived chondrocytes produced from either auricular cartilage or costal cartilage biopsies combined with adipose-derived mesenchymal stem cells. Cartilage formation was measured within the construct in vitro , and cartilage maturation and stabilization were observed 12 weeks after its subcutaneous implantation into a murine model. The proposed technology is simple and effective and is expected to improve aesthetic outcomes and reduce patient discomfort.
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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".