Oligonucleotide therapeutics, including antisense oligonucleotides and small interfering RNA, offer promising avenues for modulating the expression of disease-associated proteins. However, challenges such as nuclease degradation, poor cellular uptake, and unspecific targeting hinder their application. To overcome these obstacles, spherical nucleic acids have emerged as versatile tools for nucleic acid delivery in biomedical applications.
View Article and Find Full Text PDFContinual attempts have been made to discover excellent nonlinear optical (NLO) materials. Here, we investigate the role of stacking interactions and van der Waals forces in the designed parallel stacked complexes M[9C]M (where M = Li, Na, K, Be, Mg, and Ca) using various quantum chemical and molecular dynamics methods. The thermodynamic stability of the present complexes is also revealed by the computed interaction energy, enthalpy of formation, and Gibbs free energy of formation (Δ).
View Article and Find Full Text PDFCardiac failure can be a life-threatening condition that, if left untreated, can have significant consequences. Functional hydrogel has emerged as a promising platform for cardiac tissue engineering. In the proposed study, gelatin methacrylate (GelMA) and alginate, as a primary matrix to maintain cell viability and proliferation, and polypyrrole and carboxyl-graphene, to improve mechanical and electrical properties, are thoroughly evaluated.
View Article and Find Full Text PDFLow adsorption capacity and weak mechanical stability are the main drawbacks of chitosan (CS)-based adsorptive membranes for heavy metal ion removal. Polyvinyl alcohol (PVA) has been used to improve the mechanical stability of CS membranes, but adsorption capacity is disregarded. In the current study, the surface of the chitosan/polyvinyl alcohol (CP) membrane was modified using carboxymethyl cellulose (CMC) to increase its heavy metal ion adsorption capacity.
View Article and Find Full Text PDFComput Med Imaging Graph
December 2023
Introduction: Low-dose and fast PET imaging (low-count PET) play a significant role in enhancing patient safety, healthcare efficiency, and patient comfort during medical imaging procedures. To achieve high-quality images with low-count PET scans, effective reconstruction models are crucial for denoising and enhancing image quality. The main goal of this paper is to develop an effective and accurate deep learning-based method for reconstructing low-count PET images, which is a challenging problem due to the limited amount of available data and the high level of noise in the acquired images.
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