Mechanically, the brain is characterized by both solid and fluid properties. The resulting unique material behavior fosters proliferation, differentiation, and repair of cellular and vascular networks, and optimally protects them from damaging shear forces. Magnetic resonance elastography (MRE) is a noninvasive imaging technique that maps the mechanical properties of the brain in vivo. MRE studies have shown that abnormal processes such as neuronal degeneration, demyelination, inflammation, and vascular leakage lead to tissue softening. In contrast, neuronal proliferation, cellular network formation, and higher vascular pressure result in brain stiffening. In addition, brain viscosity has been reported to change with normal blood perfusion variability and brain maturation as well as disease conditions such as tumor invasion. In this article, the contributions of the neuronal, glial, extracellular, and vascular networks are discussed to the coarse-grained parameters determined by MRE. This reductionist multi-network model of brain mechanics helps to explain many MRE observations in terms of microanatomical changes and suggests that cerebral viscoelasticity is a suitable imaging marker for brain disease.
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http://dx.doi.org/10.1002/advs.202402338 | 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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