Mechanical stimulation enhances integration in an in vitro model of cartilage repair
Bibliographic record
Abstract
PURPOSE: (1) To characterize the effects of mechanical stimulation on the integration of a tissue-engineered construct in terms of histology, biochemistry and biomechanical properties; (2) to identify whether cells of the implant or host tissue were critical to implant integration; and (3) to study cells believed to be involved in lateral integration of tissue-engineered cartilage to host cartilage. We hypothesized that mechanical stimulation would enhance the integration of the repair implant with host cartilage in an in vitro integration model. METHODS: Articular cartilage was harvested from 6- to 9-month-old bovine metacarpal-phalangeal joints. Constructs composed of tissue-engineered cartilage implanted into host cartilage were placed in spinner bioreactors and maintained on a magnetic stir plate at either 0 (static control) or 90 (experimental) rotations per minute (RPM). The constructs from both the static and spinner bioreactors were harvested after either 2 or 4 weeks of culture and evaluated histologically, biochemically, biomechanically and for gene expression. RESULTS: The extent and strength of integration between tissue-engineered cartilage and native cartilage improved significantly with both time and mechanical stimulation. Integration did not occur if the implant was not viable. The presence of stimulation led to a significant increase in collagen content in the integration zone between host and implant at 2 weeks. The gene profile of cells in the integration zone differs from host cartilage demonstrating an increase in the expression of membrane type 1 matrix metalloproteinase (MT1-MMP), aggrecan and type II collagen. CONCLUSIONS: This study shows that the integration of in vitro tissue-engineered implants with host tissue improves with mechanical stimulation. The findings of this study suggests that consideration should be given to implementing early loading (mechanical stimulation) into future in vivo studies investigating the long-term viability and integration of tissue-engineered cartilage for the treatment of cartilage injuries. This could simply be done through the use of continuous passive motion (CPM) in the post-operative period or through a more complex and structured rehabilitation program with a gradual increase in forces across the joint over time.
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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 | 0.000 |
| 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) | 0.002 | 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".