Tissue Reorganization in Response to Mechanical Load Increases Functionality
Bibliographic record
Abstract
In the rapidly growing field of tissue engineering, the functional properties of tissue substitutes are recognized as being of the utmost importance. The present study was designed to evaluate the effects of static mechanical forces on the functionality of the produced tissue constructs. Living tissue sheets reconstructed by the self-assembly approach from human cells, without the addition of synthetic material or extracellular matrix (ECM), were subjected to mechanical load to induce cell and ECM alignment. In addition, the effects of alignment on the function of substitutes reconstructed from these living tissue sheets were evaluated. Our results show that tissue constructs made from living tissue sheets, in which fibroblasts and ECM were aligned, presented higher mechanical resistance. This was assessed by the modulus of elasticity and ultimate strength as compared with tissue constructs in which components were randomly oriented. Moreover, tissue-engineered vascular media made from a prealigned living tissue sheet, produced with smooth muscle cells, possessed greater contractile capacity compared with those produced from living tissue sheets that were not prealigned. These results show that the mechanical force generated by cells during tissue organization is an asset for tissue component alignment. Therefore, this work demonstrates a means to improve the functionality (mechanical and vasocontractile properties) of tissues reconstructed by tissue engineering by taking advantage of the biomechanical forces generated by cells under static strain.
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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) | 0.002 | 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".