Effects of mechanical loading on human mesenchymal stem cells for cartilage tissue engineering
Bibliographic record
Abstract
Today, articular cartilage damage is a major health problem, affecting people of all ages. The existing conventional articular cartilage repair techniques, such as autologous chondrocyte implantation (ACI), microfracture, and mosaicplasty, have many shortcomings which negatively affect their clinical outcomes. Therefore, it is essential to develop an alternative and efficient articular repair technique that can address those shortcomings. Cartilage tissue engineering, which aims to create a tissue-engineered cartilage derived from human mesenchymal stem cells (MSCs), shows great promise for improving articular cartilage defect therapy. However, the use of tissue-engineered cartilage for the clinical therapy of articular cartilage defect still remains challenging. Despite the importance of mechanical loading to create a functional cartilage has been well demonstrated, the specific type of mechanical loading and its optimal loading regime is still under investigation. This review summarizes the most recent advances in the effects of mechanical loading on human MSCs. First, the existing conventional articular repair techniques and their shortcomings are highlighted. The important parameters for the evaluation of the tissue-engineered cartilage, including chondrogenic and hypertrophic differentiation of human MSCs are briefly discussed. The influence of mechanical loading on human MSCs is subsequently reviewed and the possible mechanotransduction signaling is highlighted. The development of non-hypertrophic chondrogenesis in response to the changing mechanical microenvironment will aid in the establishment of a tissue-engineered cartilage for efficient articular cartilage repair.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".