13 research outputs found

    Exploration of Zinc Oxide Nanoparticles as a Multitarget and Multifunctional Anticancer Nanomedicine

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    Because of the complexity of cancer, an ideal anticancer strategy is better to target both cancer cells and the tumor microenvironment. In this study, for the first time, we demonstrated that zinc oxide nanoparticles (ZnO NPs) were able to target multiple cell types of cancer, including cancer cells, cancer stem cells (CSCs), and macrophages, and simultaneously perform several key functions, including inhibition of cancer proliferation, sensitization of drug-resistant cancer, prevention of cancer recurrence and metastasis, and resuscitation of cancer immunosurveillance. As a nanocarrier, the chemotherapy drug, doxorubicin (Dox), could be loaded to ZnO NPs and the Dox-loaded ZnO NPs (ZnO/Dox) possessed excellent physicochemical and pH-responsive drug release properties. ZnO/Dox could be effectively internalized by both drug-sensitive and multidrug resistant (MDR) cancer cells and penetrate more efficiently through three-dimensional (3D) cancer cell spheroids compared with free Dox. As a cytotoxic agent, ZnO NPs were more efficient to kill MDR cancer cells. Interestingly, neither ZnO nor Dox showed high cytotoxicity in the 3D cancer cell spheroids, whereas ZnO/Dox showed remarkable synergistic anticancer effects. More importantly, we demonstrated that ZnO NPs could effectively downregulate CD44, a key CSC surface marker, and decrease the stemness of CSCs, leading to the sensitization of the Dox treatment, inhibition of the cancer cell adhesion and migration, and prevention of the tumor (3D cancer cell spheroid) formation. As an immunomodulator, ZnO NPs could protect macrophages from the Dox-induced toxicity and boost the Dox-induced macrophage polarization toward an M1-like phenotype. The macrophage-conditioned medium could promote the cancer cell apoptosis in both cancer cell monolayers and 3D spheroids. The findings in this study indicated that ZnO NPs were a multifunctional and multitarget nanocarrier and nanomedicine that would have more profound effects on cancer treatment

    Logistic Regression Results for predicting symptomatic stroke.

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    <p><i>CMB</i> cerebral microbleeds; <i>OR</i> odds ratio; <i>CI</i> confidence interval.</p><p><i>nWMHs</i> normalized white matter hyperintensities</p><p>Logistic Regression Results for predicting symptomatic stroke.</p

    T2*-weighted gradient image of a patient (aged 66 years) with clinically overt stroke (A-C).

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    <p>Multiple cerebral microbleeds (CMBs) were observed in the basal ganglia and thalamus (A, B). In addition, CMBs were observed in subcortical areas in both parietal lobes (C). T2*-weighted gradient image of a patient (aged 62 years) without clinically overt stroke (D-E). A single cerebral microbleed was observed in subcortical areas in the left parietal lobe (E).</p

    Frequency of CMBs according to presence of symptomatic stroke.

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    <p>n (%) value</p><p><i>CMBs</i> cerebral microbleeds</p><p>Frequency of CMBs according to presence of symptomatic stroke.</p

    Baseline characteristics of patients classified according to presence or absence of CMBs.

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    <p><i>CADASIL</i> cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy</p><p>Data are mean ± SD or n (%) values.</p><p>Baseline characteristics of patients classified according to presence or absence of CMBs.</p

    Demographic and clinical characteristics of CADASIL in patients with the R544C mutation.

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    <p>Data are mean ± SD or n (%) values.</p><p><i>CADASIL</i> cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy</p><p><i>nWMHs</i> normalized White Matter Hyperintensities.</p><p>Demographic and clinical characteristics of CADASIL in patients with the R544C mutation.</p

    Supramolecular Cyclodextrin Supplements to Improve the Tissue Adhesion Strength of Gelatin Bioglues

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    A practical strategy to strengthen the inherent tissue adhesiveness of bioglues was investigated without compromising their chemical structure. A simple blending of α-cyclodextrins (α-CDs) in gelatin glues substantially improved the adhesiveness of the glues upon contact with porcine skin. Interestingly, the adhesiveness was even further enhanced when the glues were supplemented with γ-CDs through the formation of multivalent supramolecular networks inside of the glue. In contrast, the effect of β-CDs was rather limited because their water solubility is relatively low. A model study using modified gold substrates demonstrated that CDs also interacted with biomolecules naturally present in skin, thereby improving the adhesiveness. Altogether, we suggest that the supramolecular networking of CDs, both inside the glue and at the glue–skin interface, could reinforce tissue adhesiveness of bioglues, which allows them to be of great relevance to a primary treatment modality for larger skin incisions

    <i>Autographivirinae</i> subfamily phages are represented as an independent network.

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    <p>Each node is illustrated as a pie chart, with the wedges representing the proportion of edges connected with nodes belonging to each cluster. The left panel shows the clusters in different colors. The lower panel represents the membership matrix. The rows indicate phages related to <i>Autographivirinae</i> subfamily and the columns represent the clusters that share wedges with the nodes more than 0.01. Shared boxes indicate the clusters into which the phages were assigned by the MCL algorithm.</p

    Comparative genome analysis among KBNP1315 and select relatives (KBNP1315, SP6, K1E, K1-5, and ACG-c91).

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    <p>Each ORF is represented by a color assigned according to the protein function. The degree of amino acid similarity between paired ORFs was analyzed by BLASTp search, and the percentage of similarity is represented in color from pale gray to dark gray.</p

    Protein-sharing network of KBNP1315.

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    <p>A network for KBNP1315 was produced using Cytoscape 3.1.2. Nodes represent phages, while edges indicate significant relationships between nodes. Each node is colored in accordance with its ICTV classification.</p
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