Implantation of Scaffold‐Free Engineered Cartilage Constructs in a Rabbit Model for Chondral Resurfacing
Bibliographic record
Abstract
Joint resurfacing techniques offer an attractive treatment for damaged or diseased cartilage, as this tissue characteristically displays a limited capacity for self-repair. While tissue-engineered cartilage constructs have shown efficacy in repairing focal cartilage defects in animal models, a substantial number of cells are required to generate sufficient quantities of tissue for the repair of larger defects. In a previous study, we developed a novel approach to generate large, scaffold-free cartilaginous constructs from a small number of donor cells (20 000 cells to generate a 3-cm(2) tissue construct). As comparable thicknesses to native cartilage could be achieved, the purpose of the present study was to assess the ability of these constructs to survive implantation as well as their potential for the repair of critical-sized chondral defects in a rabbit model. Evaluated up to 6 months post-implantation, allogenic constructs survived weight bearing without a loss of implant fixation. Implanted constructs appeared to integrate near-seamlessly with the surrounding native cartilage and also to extensively remodel with increasing time in vivo. By 6 months post-implantation, constructs appeared to adopt both a stratified (zonal) appearance and a biochemical composition similar to native articular cartilage. In addition, constructs that expressed superficial zone markers displayed higher histological scores, suggesting that transcriptional prescreening of constructs prior to implantation may serve as an approach to achieve superior and/or more consistent reparative outcomes. As the results of this initial animal study were encouraging, future studies will be directed toward the repair of chondral defects in more mechanically demanding anatomical locations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".