Dataset Information

Functional meniscus reconstruction with biological and biomechanical heterogeneities through topological self-induction of stem cells.

ABSTRACT: Meniscus injury is one of the most common sports injuries within the knee joint, which is also a crucial pathogenic factor for osteoarthritis (OA). The current meniscus substitution products are far from able to restore meniscal biofunctions due to the inability to reconstruct the gradient heterogeneity of natural meniscus from biological and biomechanical perspectives. Here, inspired by the topology self-induced effect and native meniscus microstructure, we present an innovative tissue-engineered meniscus (TEM) with a unique gradient-sized diamond-pored microstructure (GSDP-TEM) through dual-stage temperature control 3D-printing system based on the mechanical/biocompatibility compatible high M_w poly(ε-caprolactone) (PCL). Biologically, the unique gradient microtopology allows the seeded mesenchymal stem cells with spatially heterogeneous differentiation, triggering gradient transition of the extracellular matrix (ECM) from the inside out. Biomechanically, GSDP-TEM presents excellent circumferential tensile modulus and load transmission ability similar to the natural meniscus. After implantation in rabbit knee, GSDP-TEM induces the regeneration of biomimetic heterogeneous neomeniscus and efficiently alleviates joint degeneration. This study provides an innovative strategy for functional meniscus reconstruction. Topological self-induced cell differentiation and biomechanical property also provides a simple and effective solution for other complex heterogeneous structure reconstructions in the human body and possesses high clinical translational potential.

SUBMITTER: Du M

PROVIDER: S-EPMC10944202 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature

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Functional meniscus reconstruction with biological and biomechanical heterogeneities through topological self-induction of stem cells.

Du Mingze M Liu Kangze K Lai Huinan H Qian Jin J Ai Liya L Zhang Jiying J Yin Jun J Jiang Dong D

Bioactive materials 20240311

Meniscus injury is one of the most common sports injuries within the knee joint, which is also a crucial pathogenic factor for osteoarthritis (OA). The current meniscus substitution products are far from able to restore meniscal biofunctions due to the inability to reconstruct the gradient heterogeneity of natural meniscus from biological and biomechanical perspectives. Here, inspired by the topology self-induced effect and native meniscus microstructure, we present an innovative tissue-engineer ...[more]

PMID: 38496031

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Project description:BackgroundCommunity detection algorithms are fundamental tools to uncover important features in networks. There are several studies focused on social networks but only a few deal with biological networks. Directly or indirectly, most of the methods maximize modularity, a measure of the density of links within communities as compared to links between communities.ResultsHere we analyze six different community detection algorithms, namely, Combo, Conclude, Fast Greedy, Leading Eigen, Louvain and Spinglass, on two important biological networks to find their communities and evaluate the results in terms of topological and functional features through Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology term enrichment analysis. At a high level, the main assessment criteria are 1) appropriate community size (neither too small nor too large), 2) representation within the community of only one or two broad biological functions, 3) most genes from the network belonging to a pathway should also belong to only one or two communities, and 4) performance speed. The first network in this study is a network of Protein-Protein Interactions (PPI) in Saccharomyces cerevisiae (Yeast) with 6532 nodes and 229,696 edges and the second is a network of PPI in Homo sapiens (Human) with 20,644 nodes and 241,008 edges. All six methods perform well, i.e., find reasonably sized and biologically interpretable communities, for the Yeast PPI network but the Conclude method does not find reasonably sized communities for the Human PPI network. Louvain method maximizes modularity by using an agglomerative approach, and is the fastest method for community detection. For the Yeast PPI network, the results of Spinglass method are most similar to the results of Louvain method with regard to the size of communities and core pathways they identify, whereas for the Human PPI network, Combo and Spinglass methods yield the most similar results, with Louvain being the next closest.ConclusionsFor Yeast and Human PPI networks, Louvain method is likely the best method to find communities in terms of detecting known core pathways in a reasonable time.

Dataset Information

Functional meniscus reconstruction with biological and biomechanical heterogeneities through topological self-induction of stem cells.

Publications

Functional meniscus reconstruction with biological and biomechanical heterogeneities through topological self-induction of stem cells.

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