Effective repair of articular cartilage using human pluripotent stem cell-derived tissue
Bibliographic record
Abstract
In an effort to develop an effective source of clinically relevant cells and tissues for cartilage repair a directed differentiation method was used to generate articular chondrocytes and cartilage tissues from human embryonic stem cells (hESCs). It has previously been demonstrated that chondrocytes derived from hESCs retain a stable cartilage-forming phenotype following subcutaneous implantation in mice. In this report, the potential of hESC-derived articular-like cartilage to repair osteochondral defects created in the rat trochlea was evaluated. Articular cartilage-like tissues were generated from hESCs and implanted into the defects. After 6 and 12 weeks, the defects were evaluated histologically and immunohistochemically, and the quality of repair was assessed using a modified ICRS II scoring system. Following 6 and 12 weeks after implantation, hESC-derived cartilage tissues maintained their proteoglycan and type II collagen-rich matrix and scored significantly higher than control defects, which had been filled with fibrin glue alone. Implants were found to be well integrated with native host tissue at the basal and lateral surfaces, although implanted human cells and host cells remained regionally separated. A subset of implants underwent a process of remodeling similar to endochondral ossification, suggesting the potential for a single cartilaginous implant to promote the generation of new subchondral bone in addition to repair of the articular cartilage. The ability to create cartilage tissues with integrative and reparative properties from an unlimited and robust cell source represents a significant advance for cartilage repair that can be further developed in large animal models before clinical- setting application.
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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) | 0.000 | 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".