Cellular therapy and tissue engineering for cartilage repair
Bibliographic record
Abstract
Articular cartilage (AC) has limited capacity for repair. The first attempt to repair cartilage using tissue engineering was reported in 1977. Since then, cell-based interventions have entered clinical practice in orthopaedics, and several tissue engineering approaches to repair cartilage are in the translational pipeline towards clinical application. Classically, these involve a scaffold, substrate or matrix to provide structure, and cells such as chondrocytes or mesenchymal stromal cells to generate the tissue. We discuss the advantages and drawbacks of the use of various cell types, natural and synthetic scaffolds, multiphasic or gradient-based scaffolds, and self-organizing or self-assembling scaffold-free systems, for the engineering of cartilage constructs. Several challenges persist including achieving zonal tissue organization and integration with the surrounding tissue upon implantation. Approaches to improve cartilage thickness, organization and mechanical properties include mechanical stimulation, culture under hypoxic conditions, and stimulation with growth factors or other macromolecules. In addition, advanced technologies such as bioreactors, biosensors and 3D bioprinting are actively being explored. Understanding the underlying mechanisms of action of cell therapy and tissue engineering approaches will help improve and refine therapy development. Finally, we discuss recent studies of the intrinsic cellular and molecular mechanisms of cartilage repair that have identified novel signals and targets and are inspiring the development of molecular therapies to enhance the recruitment and cartilage reparative activity of joint-resident stem and progenitor cells. A one-fits-all solution is unrealistic, and identifying patients who will respond to a specific targeted treatment will be critical.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".