Tissue engineering for cartilage repair requires biomaterials that show rapid gelation and adequate mechanical properties. Although the use of hydrogel is the most promising biomaterial, it often lacks in rigidity and anchorage of cells when they are surrounded by synovial fluid while they are subjected to heavy loads. We developed and produced the Silk Elastin-Like co-Recombinamer (SELR), which contains both the physical interaction from elastin motifs and from silk motifs. In the first part of this work, we set up and optimized a preannealing treatment based on the evolution of silk motifs into β-sheet structures in order to fulfill the required mechanical properties of hydrogels for cartilage repair. The new preannealed SELRs (pA(EIS)-(IR)) were characterized with the combination of several experimental techniques (CD, TEM, SEM, and rheology) to provide a deep insight into the material features. Finally, the regeneration properties of the pA(EIS)-(IR) hydrogel embedded with chondrocytes were evaluated. After 4 weeks of culturing in a standardized and representative ex vivo model, the biochemical and histological analysis revealed the production of glycosaminglycans and collagen. Moreover, the immunohistochemistry showed the absence of fibro-cartilage and the presence of hyaline cartilage. Hence, we conclude that the pA(EIS)-(IR) hydrogel presents improved mechanical properties while conserving the injectability, which leads to successful regeneration of hyaline cartilage in an ex vivo model.
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http://dx.doi.org/10.1021/acs.biomac.8b01211 | DOI Listing |
Sci Rep
December 2024
Department of Physics, Indian Institute of Technology, Patna, 801106, Bihar, India.
A highly effective method for creating a supramolecular metallogel of Ni(II) ions (NiA-TA) has been developed in our work. This approach uses benzene-1,3,5-tricarboxylic acid as a low molecular weight gelator (LMWG) in DMF solvent. Rheological studies assessed the mechanical properties of the Ni(II)-metallogel, revealing its angular frequency response and thixotropic behaviour.
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December 2024
School of Electrical Engineering, Aalto University, P.O. Box 15500, Aalto, FI-00076, Finland.
Engineering plastics are finding widespread applications across a broad temperature spectrum, with additive manufacturing (AM) having now become commonplace for producing aerospace-grade components from polymers. However, there is limited data available on the behavior of plastic AM parts exposed to elevated temperatures. This study focuses on investigating the tensile strength, tensile modulus and Poisson's ratio of parts manufactured using fused filament fabrication (FFF) and polyetheretherketone (PEEK) plastics doped with two additives: short carbon fibers (SCFs) and multi-wall carbon nanotubes (MWCNTs).
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December 2024
School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, China.
The dolomite dust-emulsified asphalt composite (DAC) with excellent mechanical properties was successfully prepared using alkali activation. The effects of different alkali concentrations and emulsified asphalt contents on the mechanical properties of the materials were studied. And the micro-mechanisms of its mechanical performance changes were analyzed through SEM and XRD characterization.
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December 2024
Department of Mechanical Engineering, Sejong University, Seoul, Republic of Korea.
Nonthermal plasma has been extensively utilized in various biomedical fields, including surface engineering of medical implants to enhance their biocompatibility and osseointegration. To ensure robustness and cost effectiveness for commercial viability, stable and effective plasma is required, which can be achieved by reducing gas pressure in a controlled volume. Here, we explored the impact of reduced gas pressure on plasma properties, surface characteristics of plasma-treated implants, and subsequent biological outcomes.
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December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
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