The Performance of Composite Nanoparticles Based on Fe3O4@SiO2/PLGA/PFOB in Magnetic Resonance Imaging and Photoacoustic Imaging

Qin, Huihao; Zhang, Wei; Luo, Yi; Wei, Yubo; Sun, Shenglu; Chen, Jingya

doi:10.1166/sam.2019.3723

Cited by 7 publications

(2 citation statements)

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“…Compared to small molecular probes and conventional polymer probes, the bioimaging probes based on supramolecular polymer nanocomposites often simultaneously possess many important features, such as biocompatibility, target specificity, stimulus responsiveness, and diversity. Specific contrast agents or functional components can be incorporated as desired to prepare diverse bioimaging probes, such as fluorescent components for fluorescence imaging [ 49 , 50 ], paramagnetic components for MRI [ 51 , 52 ], infrared absorbing agents for photoacoustic (PA) imaging [ 53 ], and X-ray absorbing agents for computed tomography (CT) imaging [ 54 ].…”

Section: Biomedical Applicationsmentioning

confidence: 99%

Supramolecular Polymer Nanocomposites for Biomedical Applications

Yue

et al. 2021

Polymers

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Polymer nanocomposites, a class of innovative materials formed by polymer matrixes and nanoscaled fillers (e.g., carbon-based nanomaterials, inorganic/semiconductor nanoparticles, metal/metal-oxide nanoparticles, polymeric nanostructures, etc.), display enhanced mechanical, optoelectrical, magnetic, catalytic, and bio-related characteristics, thereby finding a wide range of applications in the biomedical field. In particular, the concept of supramolecular chemistry has been introduced into polymer nanocomposites, which creates myriad “smart” biomedical materials with unique physicochemical properties and dynamic tunable structures in response to diverse external stimuli. This review aims to provide an overview of the chemical composition, morphological structures, biological functionalities, and reinforced performances of supramolecular polymer nanocomposites. Additionally, recent advances in biomedical applications such as therapeutic delivery, bioimaging, and tissue engineering are also discussed, especially their excellent properties leveraged in the development of multifunctional intelligent biomedical materials.

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Section: Biomedical Applicationsmentioning

confidence: 99%

Supramolecular Polymer Nanocomposites for Biomedical Applications

Yue

et al. 2021

Polymers

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“…e feature extractor includes convolution filtering and downsampling [11,12]. e convolution filter kernel on the feature map is usually 3 * 3, and the downsampling operation after filtering usually has a ratio of 2. e classifier is composed of a fully connected network, and the features learned by the feature extractor will be output by the fully connected layer [13][14][15]. e purpose of this study was to use convolutional neural network as a statistical algorithm to construct a model of corneal inflammation in SD rats and to explore the therapeutic effect of insulin liposomes on corneal inflammation.…”

Section: Introductionmentioning

confidence: 99%

Using Convolutional Neural Network as a Statistical Algorithm to Explore the Therapeutic Effect of Insulin Liposomes on Corneal Inflammation

Liao

Jiang

et al. 2022

Computational Intelligence and Neuroscience

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Aiming at the disadvantages of easy recurrence of keratitis, difficult eradication by surgery, and easy bacterial resistance, insulin-loaded liposomes were prepared, and convolutional neural network was used as a statistical algorithm to build SD rat corneal inflammation model and study insulin-loaded liposomes, alleviating effect on corneal inflammatory structure in SD rats. The INS/PFOB@LIP was developed by means of thin-film dispersive phacoemulsification, its structure was monitored using a transmission electron microscope, particle size and appearance potential were monitored using a Malvern particle sizer, and ultraviolet consumption spectrum was monitored using a UV spectrophotometer. The encapsulation rate, drug loading, and distribution of insulin liposomes in rat corneal inflammatory model were measured and calculated. The cytotoxicity of liposome materials was evaluated by CCK-8 assay, and the toxic effects of insulin and insulin liposomes on cells were detected. The cornea of SD rats was burned with NaOH solution (1 mol/L), and the SD rat corneal inflammation model was created. The insulin liposome was applied to the corneal inflammation model, and the therapeutic effect of insulin liposome on corneal inflammation was evaluated by slit lamp, corneal immunohistochemistry, corneal HE staining, and corneal Sirius red staining. Insulin-loaded liposomes were successfully constructed with an average particle size of (130.69 ± 3.87) nm and a surface potential of (−38.24 ± 2.57) mV. The encapsulation rate of insulin liposomes was (48.89 ± 1.24)%, and the drug loading rate was (24.45 ± 1.24)%. The SD rat corneal inflammation model was successfully established. After insulin liposome treatment, the staining area of corneal fluorescein sodium was significantly reduced, the corneal epithelium was significantly thickened, the content of corneal collagen was increased, the expression of inflammatory factors was significantly reduced, and new blood vessels (corneal neovascularization, CNV) growth was inhibited.

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