Tissue engineering scaled-up, anatomically shaped osteochondral constructs for joint resurfacing
Bibliographic record
Abstract
Arthroplasty is currently the only surgical procedure available to restore joint function following articular cartilage and bone degeneration associated with diseases such as osteoarthritis (OA). A potential alternative to this procedure would be to tissue-engineer a biological implant and use it to replace the entire diseased joint. The objective of this study was therefore to tissue-engineer a scaled-up, anatomically shaped, osteochondral construct suitable for partial or total resurfacing of a diseased joint. To this end it was first demonstrated that a bone marrow derived mesenchymal stem cell seeded alginate hydrogel could support endochondral bone formation in vivo within the osseous component of an osteochondral construct, and furthermore, that a phenotypically stable layer of articular cartilage could be engineered over this bony tissue using a co-culture of chondrocytes and mesenchymal stem cells. Co-culture was found to enhance the in vitro development of the chondral phase of the engineered graft and to dramatically reduce its mineralisation in vivo. In the final part of the study, tissue-engineered grafts (~ 2 cm diameter) mimicking the geometry of medial femorotibial joint prostheses were generated using laser scanning and rapid prototyped moulds. After 8 weeks in vivo, a layer of cartilage remained on the surface of these scaled-up engineered implants, with evidence of mineralisation and bone development in the underlying osseous region of the graft. These findings open up the possibility of a tissue-engineered treatment option for diseases such as OA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".