Mechanical activation drives tenogenic differentiation of human mesenchymal stem cells in aligned dense collagen hydrogels
Bibliographic record
Abstract
Abstract Tendons are force transmitting mechanosensitive tissues predominantly comprised of highly aligned collagen type I fibres. In this study, the recently introduced gel aspiration-ejection method was used to rapidly fabricate aligned dense collagen (ADC) hydrogel scaffolds. ADCs provide a biomimetic environment compared to traditional collagen hydrogels that are mechanically unstable and comprised of randomly oriented fibrils. The ADC scaffolds were shown to be anisotropic with comparable stiffness to immature tendons. Furthermore, the application of static and cyclic uniaxial loading, short-term (48 h) and high-strain (20%), resulted in a 3-fold increase in both the ultimate tensile strength and modulus of ADCs. Similar mechanical activation of human mesenchymal stem cell (MSC) seeded ADCs in serum- and growth factor-free medium induced their tenogenic differentiation. Both static and cyclic loading profiles resulted in a greater than 12-fold increase in scleraxis gene expression and either suppressed or maintained osteogenic and chondrogenic expressions. Following the 48 h mechanoactivation period, the MSC-seeded scaffolds were matured by tethering in basal medium without further external mechanical stimulation for 19 days, altogether making up 21 days of culture. Extensive cell-induced matrix remodeling and deposition of collagens type I and III, tenascin-C and tenomodulin were observed, where initial cyclic loading induced significantly higher tenomodulin protein content. Moreover, the initial short-term mechanical stimulation elongated and polarized seeded MSCs and overall cell alignment was significantly increased in those under static loading. These findings indicate the regenerative potential of the ADC scaffolds for short-term mechanoactivated tenogenic differentiation, which were achieved even in the absence of serum and growth factors that may potentially increase clinical translatability. Graphical abstract
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".