Advanced Strategies for Articular Cartilage Defect Repair
Bibliographic record
Abstract
Articular cartilage is a unique tissue owing to its ability to withstand repetitive compressive stress throughout an individual's lifetime. However, its major limitation is the inability to heal even the most minor injuries. There still remains an inherent lack of strategies that stimulate hyaline-like articular cartilage growth with appropriate functional properties. Recent scientific advances in tissue engineering have made significant steps towards development of constructs for articular cartilage repair. In particular, research has shown the potential of biomaterial physico-chemical properties significantly influencing the proliferation, differentiation and matrix deposition by progenitor cells. Accordingly, this highlights the potential of using such properties to direct the lineage towards which such cells follow. Moreover, the use of soluble growth factors to enhance the bioactivity and regenerative capacity of biomaterials has recently been adopted by researchers in the field of tissue engineering. In addition, gene therapy is a growing area that has found noteworthy use in tissue engineering partly due to the potential to overcome some drawbacks associated with current growth factor delivery systems. In this context, such advanced strategies in biomaterial science, cell-based and growth factor-based therapies that have been employed in the restoration and repair of damaged articular cartilage will be the focus of this review article.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".