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Zwitterionic polymers are suitable for replacing poly(ethylene glycol) (PEG) polymers because of their better antifouling properties, but zwitterionic polymers have poor mechanical properties, strong water absorption, and their homopolymers should not be used directly. To solve these problems, a reversible-addition fragmentation chain transfer (RAFT) polymerization process was used to prepare copolymers comprised of zwitterionic side chains that were attached to an ITO glass substrate using spin-casting. The presence of 4-vinylpyridine (4VP) and zwitterion chains on these polymer-coated ITO surfaces was confirmed using 1H NMR, FTIR, and GPC analyses, with successful surface functionalization confirmed using water contact angle, X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM) studies. Changes in water contact angles and C/O ratios (XPS) analysis demonstrated that the functionalization of these polymers with β-propiolactone resulted in hydrophilic mixed 4VP/zwitterionic polymers. Protein adsorption and cell attachment assays were used to optimize the ratio of the zwitterionic component to maximize the antifouling properties of the polymer brush surface. This work demonstrated that the antifouling surface coatings could be readily prepared using a “P4VP-modified” method, that is, the functionality of P4VP to modify the prepared zwitterionic polymer. We believe these materials are likely to be useful for the preparation of biomaterials for biosensing and diagnostic applications.

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The use of a distractor is a reliable and minimally invasive method for correcting Grade I and II inverted nipples while preserving breastfeeding.

BACKGROUND: Brain tumor segmentation plays an important role in assisting diagnosis of disease, treatment plan planning, and surgical navigation. OBJECTIVE: This study aims to improve the accuracy of tumor boundary segmentation using the multi-scale U-Net network. METHODS: In this study, a novel U-Net with dilated convolution (DCU-Net) structure is proposed for brain tumor segmentation based on the classic U-Net structure. First, the MR brain tumor images are pre-processed to alleviate the class imbalance problem by reducing the input of the background pixels. Then, the multi-scale spatial pyramid pooling is used to replace the max pooling at the end of the down-sampling path. It can expand the feature receptive field while maintaining image resolution. Finally, a dilated convolution residual block is combined to improve the skip connections in the training networks to improve the network's ability to recognize the tumor details. RESULTS: The proposed model has been evaluated using the Brain Tumor Segmentation (BRATS) 2018 Challenge training dataset and achieved the dice similarity coefficients (DSC) score of 0.91, 0.78 and 0.83 for whole tumor, core tumor and enhancing tumor segmentation, respectively. CONCLUSIONS: The experiment results indicate that the proposed model yields a promising performance in automated brain tumor segmentation.