Mechano-bioengineering of the knee meniscus
Bibliographic record
Abstract
The meniscus is a fibrocartilaginous structure of the knee joint that serves a crucial role in joint health and biomechanics. Degeneration or removal of the meniscus is known to lead to a chronic and debilitating disease known as knee osteoarthritis, whose prevalence is expected to increase in the next few decades. Meniscus bioengineering has been developed as a potential alternative to current treatment methods, wherein meniscus-like tissues are engineered using cells, materials, and biomechanical stimuli. The application of mechanical stimulation in meniscus bioengineering has presented varied results but, for the most part, it has been shown to enhance meniscus-like tissue formation. In this review, we summarized literature over the last 10 years of various mechanical stimuli applied in bioengineering meniscus tissues. The role of individual loading types is examined, and the effects on engineered meniscus are evaluated on both molecular and tissue levels. In addition, simulated microgravity is highlighted as a new area of interest in meniscus engineering, and its potential use as a disease-driving platform is discussed. Taken together, with the increased understanding of the effects of mechanical stimulation on bioengineered meniscus tissues, the most suitable loading regime could be developed for meniscus tissue engineering and osteoarthritis modeling.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".