Recent Advances in Mechano-Responsive Hydrogels for Biomedical Applications
Bibliographic record
Abstract
Mechanical responsiveness is prevalent in biological systems and plays an essential role in many biomechanical processes. The past two decades have witnessed enormous effort devoted to the development of biomimetic mechano-responsive hydrogels which are capable of adapting their physical and chemical properties to external mechanical stimuli. Due to the combination of tissue similarity and mechano-responsive properties, this type of hydrogel offers great advantages for diverse biomedical applications. Strain-stiffening and self-healing hydrogels duplicate the physiological properties of biological tissues, serving as promising candidates for artificial tissues, tissue scaffolds, and wound dressings. The shear-thinning property provides the hydrogels injectability, and the regional delivery contributes to minimally invasive treatment. Mechanochromic hydrogels allow the direct visualization of mechanical stress, holding great promise in biosensing and diagnosing. This review highlights the most recent developments in mechano-responsive hydrogels with various applications in the biomedical field. Different types of mechano-responsive hydrogels are introduced with focus on their responsive mechanisms, design strategies, and in vitro/in vivo performances, providing useful insights into the understanding and future research directions of mechano-responsive hydrogels with applications in biomedical engineering.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".