Orthotopic equine study confirms the pivotal importance of structural reinforcement over the pre‐culture of cartilage implants
Bibliographic record
Abstract
In articular cartilage (AC), the collagen arcades provide the tissue with its extraordinary mechanical properties. As these structures cannot be restored once damaged, functional restoration of AC defects remains a major challenge. We report that the use of a converged bioprinted, osteochondral implant, based on a gelatin methacryloyl cartilage phase, reinforced with precisely patterned melt electrowritten polycaprolactone micrometer-scale fibers in a zonal fashion, inspired by native collagen architecture, can provide long-term mechanically stable neo-tissue in an orthotopic large animal model. The design of this novel implant was achieved via state-of-the-art converging of extrusion-based ceramic printing, melt electrowriting, and extrusion-based bioprinting. Interestingly, the cell-free implants, used as a control in this study, showed abundant cell ingrowth and similar favorable results as the cell-containing implants. Our findings underscore the hypothesis that mechanical stability is more determining for the successful survival of the implant than the presence of cells and pre-cultured extracellular matrix. This observation is of great translational importance and highlights the aptness of advanced 3D (bio)fabrication technologies for functional tissue restoration in the harsh articular joint mechanical environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".