Ultraviolet (UV) light is known to be harmful to human health and cause organic materials to undergo photodegradation. In this Research Article, bioinspired dopamine-melanin solid nanoparticles (Dpa-s NPs) and hollow nanoparticles (Dpa-h NPs) as UV-absorbers were introduced to enhance the UV-shielding performance of polymer. First, Dpa-s NPs were synthesized through autoxidation of dopamine in alkaline aqueous solution. Dpa-h NPs were prepared by the spontaneous oxidative polymerization of dopamine solution onto polystyrene (PS) nanospheres template, followed by removal of the template. Poly(vinyl alcohol) (PVA)/Dpa nanocomposite films were subsequently fabricated by a simple casting solvent. UV irradiation protocols were set up, allowing selective study of the extra-shielding effects of Dpa-s versus Dpa-h NPs. In contrast to PVA/Dpa-s films, PVA/Dpa-h films exhibit stronger UV-shielding capabilities and can almost block the complete UV region (200-400 nm). The excellent UV-shielding performance of the PVA/Dpa-h films mainly arises from multiple absorption because of the hollow structure and large specific area of Dpa-h NPs. Moreover, the wall thickness of Dpa-h NPs can be simply controlled from 28 to 8 nm, depending on the ratio between PS and dopamine. The resulting films with Dpa-h NPs (wall thickness = ∼8 nm) maintained relatively high transparency to visible light because of the thinner wall thickness. The results indicate that the prepared Dpa-h NPs can be used as a novel UV absorber for next-generation transparent UV-shielding materials.

Sepia eumelanin (SE), a biomacromolecule, was developed to prepare the excellent UV-shielding polymer material with better photostability. UV–vis transmittance spectra showed that poly­(vinyl alcohol) PVA/SE film blocked most ultraviolet light below 300 nm even with a low concentration of SE (0.5 wt %), which still kept its high transparency in the visible spectrum. Rhodamine B photodegradation measurement further confirmed the excellent UV-shielding properties of PVA/SE film. FTIR indicated that the carbonyl absorption bands resulting from phtodegradation for PVA/SE film did not change after UV exposure for 2700 h. The tensile properties of neat PVA were deceased intensely after UV irradiation; however, those of PVA/SE film were reduced a little. Moreover, AFM indicated that the surface roughness of PVA/SE film was much lower than that of a neat PVA one. It could be concluded that SE reduced the PVA degradation rate dramatically, revealing enhanced photostability of PVA/SE film. The mechanism for outstanding UV-shielding properties and photostability of PVA/SE film was illuminated, based on the formation of charge transfer complexes (CTCs) between SE and PVA, photothermal conversion, and the well-known radical scavenging capabilities of SE.

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of liver. In this paper, we propose an automatic and efficient algorithm to segment liver from 3D CT volumes. A deep image-to-image network (DI2IN) is first deployed to generate the liver segmentation, employing a convolutional encoder-decoder architecture combined with multi-level feature concatenation and deep supervision. Then an adversarial network is utilized during training process to discriminate the output of DI2IN from ground truth, which further boosts the performance of DI2IN. The proposed method is trained on an annotated dataset of 1000 CT volumes with various different scanning protocols (e.g., contrast and non-contrast, various resolution and position) and large variations in populations (e.g., ages and pathology). Our approach outperforms the state-of-the-art solutions in terms of segmentation accuracy and computing efficiency.

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