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In this study, three kinds of electrohydrodynamic atomization (EHDA) processes (electrospraying, electrospinning, and coaxial electrospinning) are implemented to create hydroxypropyl methylcellulose (HPMC) based ultra‐thin products for providing the fast dissolution of a poorly water‐soluble drug ketoprofen (KET). An EHDA apparatus, characterized by a novel spinneret, is homemade for conducting the three processes. The three types of products are electrospun nanofibers E1, electrosprayed microparticles E2, and core‐shell nanofibers E3. SEM and TEM results indicate that they have the anticipated morphologies and inner structures. X‐ray diffraction and Fourier Transform Infrared results verify that KET is mainly amorphous in all the composites due to its fine compatibility with HPMC. In vitro dissolution tests demonstrate that the drug rapid release performances has an order of E3>E1>E2≫KET powders. The fast dissolution mechanisms are suggested and the advantages of the three products are compared. The super performance of E3 in furnishing the rapid release is attributed to a synergistic action of small size (of the shell thickness), high porosity, amorphous state of drug, and the solubility of HPMC. EHDA nanostructures can support the development of nano drug delivery systems (DDSs) through tailoring the spatial distribution of drug molecules within the nano products. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.