Ultrasound-Responsive Nrf2-Targeting siRNA-Loaded Nanobubbles for Enhancing the Treatment of Melanoma

The siRNA-mediated inhibition of nuclear factor E2-related factor 2 (Nrf2) can be an attractive approach to overcome chemoresistance in various malignant tumors, including melanoma. This work aims at designing a new type of chitosan-shelled nanobubble for the delivery of siRNA against Nrf2 in combination with an ultrasound. A new preparation method based on a water–oil–water (W/O/W) double-emulsion was purposely developed for siRNA encapsulation in aqueous droplets within a nanobubble core. Stable, very small NB formulations were obtained, with sizes of about 100 nm and a positive surface charge. siRNA was efficiently loaded in NBs, reaching an encapsulation efficiency of about 90%. siNrf2-NBs downregulated the target gene in M14 cells, sensitizing the resistant melanoma cells to the cisplatin treatment. The combination with US favored NB cell uptake and transfection efficiency. Based on the results, nanobubbles have shown to be a promising US responsive tool for siRNA delivery, able to overcome chemoresistance in melanoma cancer cells

Helcococcus kunzii isolated from a sow with purulent urocystitis

Helcococcus kunzii has never been reported in veterinary medicine. The isolation of H. kunzii from a sow with purulent urocystitis is described, suggesting this organism's potential pathogenic role in swine

Morganella morganii septicemia and concurrent renal crassicaudiasis in a Cuvier’s beaked whale (Ziphius cavirostris) stranded in Italy

Information regarding bacterial diseases in Cuvier's beaked whale (CBW, Ziphius cavirostris) is scattered and mostly incomplete. This report describes a case of septicemia by Morganella morganii in a juvenile male CBW with concurrent renal crassicaudiasis. The animal stranded along the Ligurian coastline (Italy) and underwent a systematic post-mortem examination to determine the cause of death. Histopathology showed lesions consistent with a septicemic infection, severe meningoencephalitis, and renal crassicaudiasis. An M. morganii alpha-hemolytic strain was isolated in pure culture from liver, lung, prescapular lymph node, spleen, hepatic and renal abscesses, and central nervous system (CNS). The antimicrobial susceptibility profile of the strain was evaluated with the minimum inhibitory concentrations (MICs) method and reduced susceptibility to Trimethoprim-Sulfamethoxazole is reported. Crassicauda sp. nematodes were retrieved from both kidneys. No other pathogens were detected by immunohistochemistry, serology, or biomolecular analyses. Toxicological investigations detected high concentrations of immunosuppressant pollutants in the blubber. The chronic parasitic infestation and the toxic effects of xenobiotics likely compromised the animal's health, predisposing it to an opportunistic bacterial infection. To our knowledge, this is the first description of M. morganii septicemia with CNS involvement in a wild cetacean

Extracellular electrical signals in a neuron-surface junction: model of heterogeneous membrane conductivity

Signals recorded from neurons with extracellular planar sensors have a wide range of waveforms and amplitudes. This variety is a result of different physical conditions affecting the ion currents through a cellular membrane. The transmembrane currents are often considered by macroscopic membrane models as essentially a homogeneous process. However, this assumption is doubtful, since ions move through ion channels, which are scattered within the membrane. Accounting for this fact, the present work proposes a theoretical model of heterogeneous membrane conductivity. The model is based on the hypothesis that both potential and charge are distributed inhomogeneously on the membrane surface, concentrated near channel pores, as the direct consequence of the inhomogeneous transmembrane current. A system of continuity equations having non-stationary and quasi-stationary forms expresses this fact mathematically. The present work performs mathematical analysis of the proposed equations, following by the synthesis of the equivalent electric element of a heterogeneous membrane current. This element is further used to construct a model of the cell-surface electric junction in a form of the equivalent electrical circuit. After that a study of how the heterogeneous membrane conductivity affects parameters of the extracellular electrical signal is performed. As the result it was found that variation of the passive characteristics of the cell-surface junction, conductivity of the cleft and the cleft height, could lead to different shapes of the extracellular signals

Spectral Clustering with Graph Neural Networks for Graph Pooling

Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph. SC can be used in Graph Neural Networks (GNNs) to implement pooling operations that aggregate nodes belonging to the same cluster. However, the eigendecomposition of the Laplacian is expensive and, since clustering results are graph-specific, pooling methods based on SC must perform a new optimization for each new sample. In this paper, we propose a graph clustering approach that addresses these limitations of SC. We formulate a continuous relaxation of the normalized minCUT problem and train a GNN to compute cluster assignments that minimize this objective. Our GNN-based implementation is differentiable, does not require to compute the spectral decomposition, and learns a clustering function that can be quickly evaluated on out-of-sample graphs. From the proposed clustering method, we design a graph pooling operator that overcomes some important limitations of state-of-the-art graph pooling techniques and achieves the best performance in several supervised and unsupervised tasks

Graph Neural Networks in TensorFlow and Keras with Spektral

Graph neural networks have enabled the application of deep learning to problems that can be described by graphs, which are found throughout the different fields of science, from physics to biology, natural language processing, telecommunications or medicine. In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to provide the essential building blocks for creating graph neural networks, focusing on the guiding principles of user-fr iendliness and quick prototyping on which Keras is based. Spektral is, therefore, suitable for absolute beginners and expert deep learning practitioners alike. In this work, we present an overview of Spektral’s features and report the performance of the methods implemented by the library in scenarios of node classification, graph classification, and graph regression