Mussel‐Derived Bioadaptive Artificial Tendon Facilitates the Cell Proliferation and Tenogenesis to Promote Tendon Functional Reconstruction
Bibliographic record
Abstract
Tendon injuries range from acute-related trauma to chronic-related injuries are prevalent and bring substantial pain, functional loss, and even disability to the patients. The management of tendon injuries is tricky due to the innate limited regenerative capability of the tendon. Currently, surgical intervention of tendon injuries with artificial tendons remains the standard of care. However, most of artificial tendons are manufactured with synthetic materials, which possess relatively poor biomimetic characteristics and inadequate inherent biodegradability, hence rendering limited cell proliferation and migration for tendon healing. To address these limitations, this work develops a mussel-derived artificial tendon based on double-cross-linked chitosan modification. In this design, decellularized artificial tendon serves as a natural biomimetic scaffold to facilitate the migration and adhesion of tendon repair cells. Additionally, as the cells proliferate, the artificial tendon can be degraded to facilitate tendon regeneration. Moreover, the chitosan cross-linking further enhances the mechanical strength of artificial tendon and offers a controllable degradation. The in vitro and in vivo experimental results demonstrate that mussel-derived artificial tendon not only accelerate the tendon functional reconstruction but also enable harmless clearance at postimplantation. The finding provides a promising alternative to conventional artificial tendons and spurs a new frontier to explore nature-derived artificial tendons.
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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".