Multi-axial mechanical stimulation of tissue engineered cartilage: Review
Bibliographic record
Abstract
The development of tissue engineered cartilage is a promising new approach for the repair of damaged or diseased tissue. Since it has proven difficult to generate cartilaginous tissue with properties similar to that of native articular cartilage, several studies have used mechanical stimuli as a means to improve the quantity and quality of the developed tissue. In this study, we have investigated the effect of multi-axial loading applied during in vitro tissue formation to better reflect the physiological forces that chondrocytes are subjected to in vivo. Dynamic combined compression-shear stimulation (5% compression and 5% shear strain amplitudes) increased both collagen and proteoglycan synthesis (76 +/- 8% and 73 +/- 5%, respectively) over the static (unstimulated) controls. When this multi-axial loading condition was applied to the chondrocyte cultures over a four week period, there were significant improvements in both extracellular matrix (ECM) accumulation and the mechanical properties of the in vitro-formed tissue (3-fold increase in compressive modulus and 1.75-fold increase in shear modulus). Stimulated tissues were also significantly thinner than the static controls (19% reduction) suggesting that there was a degree of ECM consolidation as a result of long-term multi-axial loading. This study demonstrated that stimulation by multi-axial forces can improve the quality of the in vitro-formed tissue, but additional studies are required to further optimize the conditions to favour improved biochemical and mechanical properties of the developed tissue.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".