Comparisons of Auricular Cartilage Tissues from Different Species
Bibliographic record
Abstract
OBJECTIVES: Tissue engineering of auricular cartilage has great potential in providing readily available materials for reconstructive surgeries. As the field of tissue engineering moves forward to developing human tissues, there needs to be an interspecies comparison of the native auricular cartilage in order to determine a suitable animal model to assess the performance of engineered auricular cartilage in vivo. METHODS: Here, we performed interspecies comparisons of auricular cartilage by comparing tissue microstructure, protein localization, biochemical composition, and mechanical properties of auricular cartilage tissues from rat, rabbit, pig, cow, and human. RESULTS: Human, pig, and cow auricular cartilage have smaller lacunae compared to rat and rabbit cartilage ( P < .05). Despite differences in tissue microstructure, human auricular cartilage has similar biochemical composition to both rat and rabbit. Auricular cartilage from pig and cow, alternatively, display significantly higher glycosaminoglycan and collagen contents compared to human, rat, and rabbit ( P < .05). The mechanical properties of human auricular cartilage were comparable to that of all 4 animal species. CONCLUSIONS: This is the first study that compares the microstructural, biochemical, and mechanical properties of auricular cartilage from different species. This study showed that different experimental animal models of human auricular cartilage may be suitable in different cases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".