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Chameleon-inspired molecular imprinted polymer with bicolored states for visual and stable detection of diethylstilbestrol in water and food samples.

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Department of Nutrition and Food Hygiene, School of Public Health, Tianjin Medical University, 300070 Tianjin, People's Republic of China; Tianjin Key Laboratory of Environment, Nutrition and Public Health, Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin, People's Republic of China. Electronic address:

A novel biomimetic molecular imprinted polymer chip with fluorescence (FL) and structural (STR) states, inspired by color patterns of chameleon skin, is fabricated for detecting diethylstilbestrol (DES). The chip features a regularly structured, non-closed-packed (NCP) colloidal photonic crystal (CPC) lattice made monodisperse MIP spheres containing fluorescence poly ionic liquid (FPIL) pigments. The FL color originates from FPIL pigments and is further enhanced by the Purcell effect, while the STR color results from the periodic arrangement of the NCP CPC structure.

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Digital Interventions for Promoting Skin Self-Examination Behaviors of Melanoma Survivors: A Systematic Review.

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Author Affiliations: Henan Vocational College of Nursing, Anyang, China (Ms Li); School of Nursing and Health, Henan University (Mr Yang), Kaifeng City, China; Nursing Department, Jiaozuo People's Hospital, Jiaozuo, China (Ms Qu); Director's Office, Zhengzhou Sixth People's Hospital, Zhengzhou, China (Ms Fu); and Nursing Department, Zhengzhou Third People's Hospital, Zhengzhou, China (Ms Niu).

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Background: Private-part skin diseases (PPSDs) can cause a patient's stigma, which may hinder the early diagnosis of these diseases. Artificial intelligence (AI) is an effective tool to improve the early diagnosis of PPSDs, especially in preventing the deterioration of skin tumors in private parts such as Paget disease. However, to our knowledge, there is currently no research on using AI to identify PPSDs due to the complex backgrounds of the lesion areas and the challenges in data collection.

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