Recent Advances in Mechanically Loaded Human Mesenchymal Stem Cells for Bone Tissue Engineering
Bibliographic record
Abstract
Large bone defects are a major health concern worldwide. The conventional bone repair techniques (e.g., bone-grafting and Masquelet techniques) have numerous drawbacks, which negatively impact their therapeutic outcomes. Therefore, there is a demand to develop an alternative bone repair approach that can address the existing drawbacks. Bone tissue engineering involving the utilization of human mesenchymal stem cells (hMSCs) has recently emerged as a key strategy for the regeneration of damaged bone tissues. However, the use of tissue-engineered bone graft for the clinical treatment of bone defects remains challenging. While the role of mechanical loading in creating a bone graft has been well explored, the effects of mechanical loading factors (e.g., loading types and regime) on clinical outcomes are poorly understood. This review summarizes the effects of mechanical loading on hMSCs for bone tissue engineering applications. First, we discuss the key assays for assessing the quality of tissue-engineered bone grafts, including specific staining, as well as gene and protein expression of osteogenic markers. Recent studies of the impact of mechanical loading on hMSCs, including compression, perfusion, vibration and stretching, along with the potential mechanotransduction signalling pathways, are subsequently reviewed. Lastly, we discuss the challenges and prospects of bone tissue engineering applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".