Converting Muscle-mimetic Biomaterials to Cartilage-like Materials
Bibliographic record
Abstract
Summary Load-bearing tissues, such as muscle and cartilage, exhibit mechanical properties that often combine high elasticity, high toughness and fast recovery, despite their different stiffness (∼100 kPa for muscles and one to several MPa for cartilage). 1-7 The advance in protein engineering and protein mechanics has made it possible to engineer protein-based biomaterials to mimic soft load-bearing tissues, such as muscles. 8-10 However, it is challenging to engineer protein biomaterials to achieve the mechanical properties exhibited by stiff tissues, such as articular cartilage, 6,11 or to develop stiff synthetic extracellular matrices for cartilage stem/progenitor cell differentiation 12 . By employing physical entanglements 13 of protein chains and force-induced protein unfolding, 14,15 here we report the engineering of a highly tough and stiff protein hydrogel to mimic articular cartilage. By crosslinking an engineered artificial elastomeric protein from its unfolded state, we introduced chain entanglement into the hydrogel network. Upon renaturation, the entangled protein chain network and forced protein unfolding entailed this single network protein hydrogel with superb mechanical properties in both tensile and compression tests, showing a Young’s modulus of ∼0.7 MPa and toughness of 250 kJ/m 3 in tensile testing; and ∼1.7 MPa in compressive modulus and toughness of 3.2 MJ/m 3 . The energy dissipation in both tensile and compression tests is reversible and the hydrogel can recovery its mechanical properties rapidly. Moreover, this hydrogel can withstand a compression stress of >60 MPa without failure, amongst the highest compressive strength achieved by a hydrogel. These properties are comparable to those of articular cartilage, making this protein hydrogel a novel cartilage-mimetic biomaterial. Our study opened up a new potential avenue towards engineering protein hydrogel-based substitute for articular cartilage, and may also help develop protein biomaterials with superb mechanical properties for applications in soft actuators and robotics.
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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".