Search

To overcome the low concentration and matrix complexity of N1-methylnicotinamide (MeNam) in biological samples, this work developed a method for the detection of MeNam in urine samples using a Fe3O4@hydroxypropylmethylcellulose@2,3-dimercaptosuccinic acid (Fe3O4@HPMC@DMSA) magnetic fluid as a pre-treatment material in combination with high performance liquid chromatography. The novel adsorbent material exhibited ultra-fast adsorption kinetics and a large adsorption capacity because Fe3O4@HPMC@DMSA NPs acted as a homogeneous magnetic fluid with more varieties and quantities of interaction sites (hydrogen-bonding interaction and electrostatic interaction). The limit of determination for MeNam was 0.05 μg mL−1 with Fe3O4@HPMC@DMSA NPs. Recoveries of spiked urine samples ranged between 95.49% and 106.4%, indicating that the developed method was promising for the accurate quantification of trace amounts of MeNam in urine samples. It provides research value for the rapid assessment of the nutritional effects of NMN dietary supplements and offers a sensitive assay for metabolism studies of related functional foods. [ABSTRACT FROM AUTHOR]

Personalised recommendation is a difficult problem that has received a lot of attention to academia and industry. Because of the sparse user–item interaction, cold-start recommendation has been a particularly difficult problem. Some efforts have been made to solve the cold-start problem by using model-agnostic meta-learning on the level of the model and heterogeneous information networks on the level of data. Moreover, using the memory-augmented meta-optimisation method effectively prevents the meta-learning model from entering the local optimum. As a result, this paper proposed memory-augmented meta-learning on meta-path, a new meta-learning method that addresses the cold-start recommendation on the meta-path furthered. The meta-path builds at the data level to enrich the relevant semantic information of the data. To achieve fast adaptation, semantic-specific memory is utilised to conduct the model with semantic parameter initialisation, and the method is optimised by a meta-optimisation method. We put this method to the test using two widely used recommended data set and three cold-start scenarios. The experimental results demonstrate the efficiency of our proposed method. [ABSTRACT FROM AUTHOR]