Nicotinamide adenine dinucleotide (NAD) is a critical regulator of metabolic networks, and declining levels of its oxidized form, NAD + , are closely associated with numerous diseases. While supplementing cells with precursors needed for NAD + synthesis has shown poor efficacy in combatting NAD + decline, an alternative strategy is the development of synthetic materials that catalyze the oxidation of NADH into NAD + , thereby taking over the natural role of the NADH oxidase (NOX) present in bacteria. Herein, we discovered that metal-nitrogen-doped graphene (MNGR) materials can catalyze the oxidation of NADH into NAD + . Among MNGR materials with different transition metals, Fe-, Co-, and Cu-NGR displayed strong catalytic activity combined with >80% conversion of NADH into NAD + , similar specificity to NOX for abstracting hydrogen from the pyridine ring of nicotinamide, and higher selectivity than 51 other nanomaterials. The NOX-like activity of FeNGR functioned well in diverse cell lines. As a proof of concept of the in vivo application, we showed that FeNGR could specifically target the liver and remedy the metabolic flux anomaly in obesity mice with NAD + -deficient cells. Overall, our study provides a distinct insight for exploration of drug candidates by design of synthetic materials to mimic the functions of unique enzymes (e.g., NOX) in bacteria.
One of the main purposes of music recommendation system is how to recommend the songs that users expect from the massive song data. Most people will use the search function of the software to search for some singers or favorite song categories they have known before. However, the search results do not consider that users are different individuals and have different preferences for songs, which leads to low user satisfaction. Driven by big data, this article proposes a individuation recommendation algorithm for pop music based on deep learning. At present, the music resources on the Internet are extremely rich, and users of various music platforms are facing the troubles of too many kinds of music and difficult to express their emotions while enjoying the leisure time brought by music. By analyzing the music files in the system and the massive user behavior records saved, the user's interest preferences are obtained, and personalized music service content is provided to users. The simulation results show that the individuation recommendation algorithm of pop music in this article is better than the traditional Collaborative Filtering (CF) in recommendation accuracy and user rating.
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