Mesenchymal stem cell-based tissue engineering strategies for repair of articular cartilage.
Bibliographic record
Abstract
Restoration of articular cartilage function and structure following pathological or traumatic damage is still considered a challenging problem in the orthopaedic field. Currently, tissue engineering-based reconstruction of articular cartilage is a feasible and continuously developing strategy to restore structure and function. Successful articular cartilage tissue engineering strategy relies largely on several essential components including cellular component, supporting 3D carrier scaffolding matrix, bioactive agents, proper physical stimulants, and safe gene delivery. Designing the right formulations from these components remain the main concern of the orthopaedic community. Utilization of mesenchymal stem cells (MSCs) for articular cartilage tissue engineering is continuously increasing compared to use of chondrocytes. Various sources of MSCs have been investigated including adipose tissue, amniotic fluid, blood, bone marrow, dermis, embryonic stem cells, infrapatellar fat pad, muscle, periosteum, placenta, synovium, trabecular bone, and umbilical cord. MSCs derived from bone marrow and umbilical cord are currently in different phases of clinical trials. A wide range of matrices have been investigated to develop tissue engineering-based strategies including carbohydrate-based scaffolds (agarose, alginate, chitosan/chitin, and hyaluronate), protein-based scaffolds (collagen, fibrin, and gelatin), and artificial polymers (polyglycolic acid, polylactic acid, poly(lactic-co-glycolic acid), polyethylene glycol, and polycaprolactone). Collagen-based scaffolds and photopolymerizable PEG-based scaffolds are currently in different phases of clinical trials. TGF-β1, TGF-β3, BMP-2, and hypoxic environment are the recommended bioactive agents to induce optimum chondrogenesis of MSCs, while TGF-β1, TGF-β3, SOX-9, BMP-2, and BMP-7 genes are the best candidate for gene delivery to MSCs. Electromagnetic field and the combination of shear forces/dynamic compression are the best maturation-promoting physical stimulants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".