Mechanical force modulates scleraxis expression in bioartificial tendons.
Bibliographic record
Abstract
Following tendon injury, cartilage, bone and fat metaplasia are often observed, making the optimization of tenocyte differentiation an important clinical goal. In this study we examined the effect of static and cyclic mechanical loading on the expression of genes which play a role in tenocyte differentiation and function, namely scleraxis (Scx) and Type I collagen (Col1a1), and determined the effect of varying mechanical parameters including (1) static vs dynamic load, (2) increasing strain magnitude, (3) inclusion of 10 s rest periods, and (4) increasing cycle number. Cyclic loading resulted in a greater increase of tenocyte gene expression than static loading over 3 weeks in culture. Increasing strain levels potentiated the induction of tenocyte genes. The insertion of a 10 s rest periods further enhanced tenocyte gene expression, as did increasing repetition numbers. These results suggest that mechanical signaling exerts an important influence on the expression of genes which play a role in determining the tendon phenotype. Further work is required to confirm and extend these findings in primary cells such as resident tendon progenitor/stem cells, in order to provide an improved understanding of biology from which optimized rehabilitation programs can be developed.
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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) | 0.000 | 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) | 0.001 | 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".