Combinatorial scaffold morphologies for zonal articular cartilage engineering
Bibliographic record
Abstract
Articular cartilage lesions are a particular challenge for regenerative medicine strategies as cartilage function stems from a complex depth-dependent organization. Tissue engineering scaffolds that vary in morphology and function offer a template for zone-specific cartilage extracellular matrix (ECM) production and mechanical properties. We fabricated multi-zone cartilage scaffolds by the electrostatic deposition of polymer microfibres onto particulate-templated scaffolds produced with 0.03 or 1.0mm(3) porogens. The scaffolds allowed ample space for chondrocyte ECM production within the bulk while also mimicking the structural organization and functional interface of cartilage's superficial zone. Addition of aligned fibre membranes enhanced the mechanical and surface properties of particulate-templated scaffolds. Zonal analysis of scaffolds demonstrated region-specific variations in chondrocyte number, sulfated GAG-rich ECM, and chondrocytic gene expression. Specifically, smaller porogens (0.03mm(3)) yielded significantly higher sGAG accumulation and aggrecan gene expression. Our results demonstrate that bilayered scaffolds mimic some key structural characteristics of native cartilage, support in vitro cartilage formation, and have superior features to homogeneous particulate-templated scaffolds. We propose that these scaffolds offer promise for regenerative medicine strategies to repair articular cartilage lesions.
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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".