Strategies to Mitigate Variability in Engineering Human Nasal Cartilage
Bibliographic record
Abstract
Skin cancer and its associated treitments can have devastating consequences for survivors; this is particularly true when cancer occurs on the nose. Recent work has applied cell-based tissue engineering (TE) strategies to develop nasal cartilage constructs for reconstruction of the nose. In this study, we have generated human nasal cartilage on a clinically approved collagen scaffold to investigate the donor-to-donor variability of TE cartilage and evaluated strategies to mitigate it. We also evaluated the gene expression of the family of fibroblast growth factor receptors (FGFR1-4) and their association with tissue quality. FGFR 1 was significantly positively correlated with GAG/DNA; a measure of chondrogenic capacity. We implemented two strategies: hypoxic culture and co-culture with mesenchymal stromal cells (MSCs) to increase tissue quality. Total glycosaminoglycan (GAG) content varied significantly between donors initially, with >10-fold difference between the best and worst donor tissue. Our co-culture strategy was able to increase TE construct quality from poor quality donor tissue while supressing hypertrophy relative to MSCs alone. However, no differences were observed with the use of hypoxic culture. Tissues generated using co-culture with MSCs became vascularized and calcified in vivo, demonstrating a non-stable cartilage phenotype in co-culture and MSCs cartilage constructs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".