Incrementing the Frequency of Dynamic Strain on SMC-Cellularised Collagen-Based Scaffolds Affects Extracellular Matrix Remodeling and Mechanical Properties
Bibliographic record
Abstract
Notwithstanding the efforts injected in vascular tissue engineering in the past 30 years, the clinical translation of engineered artery constructs is far from being successful. One common approach to improve artery regeneration is the use of cyclic mechanical stimuli to guide cellular remodeling. However, there is a lack of information on the effect of cyclic strain on cells within a 3D environment. To this end, this work explored the effect of gradual increase in stimulation frequency on the response of human umbilical artery smooth muscle cells (HUASMCs) embedded in a 3D collagen matrix. The results demonstrate that, with an applied strain of 5%, the gradual increase of frequency from 0.1 to 1 Hz improved collagen remodeling by HUASMCs compared to samples constantly stimulated at 1 Hz. The expression of collagen, elastin and matrix metalloproteinase-2 (MMP-2) genes was similar at 7 days for gradual and 1 Hz samples which showed lower amounts than static counterparts. Interestingly the mechanical properties of the constructs, specifically the amplitude of the time constants and the elastic equilibrium modulus, were enhanced by gradual increase of frequency. Taken together, these results show an increase in collagen remodeling by the HUASMCs overtime under incremental cyclic mechanical strain. This work suggests that only the in-depth investigation of the effects of stimulation parameters on the behavior of vSMC under cyclic strain in a 3D environment could lead to the design of optimized control strategies for enhanced vascular tissue generation and maturation in bioreactors.
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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".