Quasi-static and dynamic mechanical properties of artificial tissue fabricated from concentrated collagen using mechano-chemical treatment
Bibliographic record
Abstract
The present study sought to develop artificial tissues possessing mechanical properties similar to those of human tendons. Collagen solution, concentrated via dialysis, was used to form a collagen gel . The gel was then subjected to mechano-chemical treatment, applying a 20-g static mechanical loading and a chemical cross-linker, genipin , to collagen gel at 37 °C for 48 h, to shape the gel into artificial tissue by inducing longitudinal alignment of collagen fibres and enhancing cross-linking between them (20gStretch tissue). To characterise mechanical and biological properties, quasi-static and dynamic tensile tests , as well as a biocompatibility test, were performed. 20gStretch tissue possessed tensile strength of 10 MPa, tangent modulus of 317 MPa, and strain at failure of 4.2 %. Stress relaxation was ∼20 %. Hysteresis loss was ∼20 %, and residual strain was ∼30 % of the magnitude of the applied strain when subjected to 2 % and 4 % cyclic stretching. These properties were significantly more tendon-like than tissues made without mechanical loading (Static tissues). Indeed, collagen fibres in 20gStretch tissue were well aligned to the loading direction, which was not observed in Static tissues. When embedded along rat Achilles tendon , 20gStretch tissues induced only a minimal inflammatory reaction and maintained tissue integrity for 12 weeks. Although only the modulus of 20gStretch tissue reached the level of human tendon, other properties, such as strength and extensibility require further improvement. Nonetheless, our method fabricated highly elastic, biocompatible, artificial tissues.
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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) | 0.000 | 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 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".