Bioinspired and smart material systems for auricular cartilage engineering: toward microenvironment-responsive and self-regulating scaffolds

Yan Gong, Haiyue Jiang, Xia Liu · Oxford University Press (OUP) · 2026

This review highlights advancements in bioinspired and smart materials for effective auricular cartilage engineering and regeneration.

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Plain English summary

The reconstruction of auricular cartilage is challenging due to its complex structure and limited ability to regenerate. This review focuses on the latest developments in smart materials and bioinspired scaffolds that mimic the natural extracellular matrix to support cartilage regeneration. It emphasizes the use of materials that can respond to various environmental stimuli, enhancing their functionality in tissue engineering.

Why this matters

This research is significant as it addresses the clinical challenge of auricular cartilage reconstruction, which is crucial for patients needing reconstructive surgery. By developing materials that can adapt to their environment, the potential for more effective and personalized medical treatments increases, improving patient outcomes.

Key findings

  • Advancements in bioinspired scaffolds that mimic extracellular matrix properties.
  • Emerging stimulus-responsive materials can adapt to environmental cues.
  • Integration of self-healing and nanotechnology in scaffold design.
  • Strategies for modulating complex microenvironments for regeneration.
  • Framework for developing next-generation scaffolds for auricular tissue.

What's new

The review presents a comprehensive overview of integrating structural biomimicry with adaptive responsiveness in material systems for cartilage engineering.

Limitations

The abstract does not provide specific experimental results or practical applications of the discussed materials.

Commercial context

The abstract does not indicate any commercial availability or readiness of the materials discussed.

Publication

Publisher
Oxford University Press (OUP)
Publication date
January 1, 2026
Research type
Paper
License
https://creativecommons.org/licenses/by/4.0/

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Method note: Summaries and ratings on this page are generated by AI from the abstract only. Read the original paper for full context. · Model: gpt-4o-mini-2024-07-18