e-space
Manchester Metropolitan University's Research Repository

    Self-assembling Peptide Hydrogels as Extracellular Matrix-Mimicking Scaffolds for Tissue Regeneration in Chronic-Degenerative Diseases

    Castillo-Díaz, Luis A, Hidalgo-Bastida, Araida, Ruiz-Pacheco, Juan A and Pérez-Martínez, Isaac O (2023) Self-assembling Peptide Hydrogels as Extracellular Matrix-Mimicking Scaffolds for Tissue Regeneration in Chronic-Degenerative Diseases. In: Peptide Bionanomaterials: From Design to Application. Springer, pp. 367-399. ISBN 9783031293597

    [img] Accepted Version
    File will be available on: 29 August 2025.
    Available under License In Copyright.

    Download (1MB)

    Abstract

    Over the past decades, scientists have aimed to overcome the limitations of current approaches to recover the integrity and functionality of tissues severely damaged by trauma or disease. Therefore, strategies that mimic the architecture and biological functions of the extracellular matrix (ECM) in vitro represent a promising route to direct the processes involved in the maintenance of such tissues. The extracellular matrix is a complex three-dimensional niche where several types of cells and biomolecules interact with each other to orchestrate the specific functions of each tissue. Within the field of nanomaterials, bioinspired self-assembling peptide hydrogels constructed from a bottom-up approach form hierarchical structures used to carry out cutting-edge research in vitro and in vivo for a better understanding of cell biology and the potential development of the novel and more accurate treatments for repairing tissues. In this chapter, we discuss the state of the art of rationale design, and the development and use of self-assembling peptide hydrogels to improve and optimize strategies intended to tackle chronic diseases affecting bone, cartilage, heart, pancreas, and nerve tissues.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    1Download
    6 month trend
    27Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record