Percutaneous device-related infection has greatly shortened
the
service period of devices and seriously reduced the quality of life
of patients. Bacteria are one of the main pathogenic factors and cannot
be effectively and conveniently eradicated by traditional strategies
(e.g., construct coatings and introduce antibiotics), due to the complex
interface among medical devices, surrounding tissue, and colonizing
bacteria. Inspired by the periodontium, a universal bacteria-defensive
hydrogel adapting to the complicated interface is fabricated by introducing
phenol-amine chemistry to a polymeric matrix of N-hydroxyethyl acrylamide (HPC hydrogels). The HPC hydrogels with
excellent toughness (2.1 MJ/m3), adhesion (10.2 and 13.2
kPa for pigskin and Ti-6Al-4V alloy, respectively), and antibacterial
property (up to 99.9% for both Escherichia coli and Staphylococcus aureus) contributed to the innate microbe
barrier via sealing the tissue–device interface and adaptive
defense to eradicate bacteria. Meanwhile, bacterial invasion experiments
demonstrate HPC hydrogels possess both a bacteria-defensive property
(up to 24 h) and cell-protecting function at the same time. Furthermore,
the biocompatibility of HPC hydrogels is verified in tests for in
vitro cytotoxicity and in vivo irritation. Hence, the designed HPC
hydrogels are considered as an emerging and universal candidate for
preventing bacterial infection and can protect the deep tissue around
a percutaneous device.
With the gradual introduction of deep learning into the field of information hiding, the capacity of information hiding has been greatly improved. Therefore, a solution with a higher capacity and a good visual effect had become the current research goal. A novel high-capacity information hiding scheme based on improved U-Net was proposed in this paper, which combined improved U-Net network and multiscale image analysis to carry out high-capacity information hiding. The proposed improved U-Net structure had a smaller network scale and could be used in both information hiding and information extraction. In the information hiding network, the secret image was decomposed into wavelet components through wavelet transform, and the wavelet components were hidden into image. In the extraction network, the features of the hidden image were extracted into four components, and the extracted secret image was obtained. Both the hiding network and the extraction network of this scheme used the improved U-Net structure, which preserved the details of the carrier image and the secret image to the greatest extent. The simulation experiment had shown that the capacity of this scheme was greatly improved than that of the traditional scheme, and the visual effect was good. And compared with the existing similar solution, the network size has been reduced by nearly 60%, and the processing speed has been increased by 20%. The image effect after hiding the information was improved, and the PSNR between the secret image and the extracted image was improved by 6.3 dB.
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