Neobavaisoflavone (NBIF), a phenolic compound isolated from Psoralea corylifolia L., possesses several significant biological properties. However, the pharmacokinetic behaviors of NBIF have been characterized as rapid oral absorption, high clearance, and poor oral bioavailability. We found that NBIF underwent massive glucuronidation and oxidation by human liver microsomes (HLM) in this study with the intrinsic clearance (CL) values of 12.43, 10.04, 2.01, and 6.99 μL/min/mg for M2, M3, M4, and M5, respectively. Additionally, the CL values of G1 and G2 by HLM were 271.90 and 651.38 μL/min/mg, respectively, whereas their respective parameters were 59.96 and 949.01 μL/min/mg by human intestine microsomes (HIM). Reaction phenotyping results indicated that CYP1A1, 1A2, 2C8, and 2C19 were the main contributors to M4 (34.96 μL/min/mg), M3 (29.45 μL/min/mg), M3 (13.16 μL/min/mg), and M2 (63.42 μL/min/mg), respectively. UGT1A1, 1A7, 1A8, and 1A9 mainly catalyzed the formation of G1 (250.87 μL/min/mg), G2 (438.15 μL/min/mg), G1 (92.68 μL/min/mg), and G2 (1073.25 μL/min/mg), respectively. Activity correlation analysis assays showed that phenacetin-N-deacetylation was strongly correlated to M3 (r = 0.860, p = 0.003) and M4 (r = 0.775, p = 0.014) in nine individual HLMs, while significant activity correlations were detected between paclitaxel-6-hydroxylation and M2 (r = 0.675, p = 0.046) and M3 (r = 0.829, p = 0.006). There was a strong correlation between β-estradiol-3-O-glucuronide and G1 (r = 0.822, p = 0.007) and G2 (r = 0.689, p = 0.040), as well as between propofol-O-glucuronidation and G1 (r = 0.768, p = 0.016) and G2 (r = 0.860, p = 0.003). Moreover, the phase I metabolism and glucuronidation of NBIF revealed marked species differences, and mice are the best animal model for investigating the metabolism of NBIF in humans. Taken together, characterization of NBIF-related metabolic pathways involving in CYP1A1, 1A2, 2C8, 2C19, and UGT1A1, 1A7, 1A8, 1A9 are helpful for understanding the pharmacokinetic behaviors and conducting in-depth pharmacological studies.

Wendan decoction, a classical traditional Chinese medicine formula consisting of six herbal medicines, has been widely used in clinical treatments for thousands of years due to the expectorant effects. However, the chemical basis of Wendan decoction remains unclear, which hinders the elucidation of the scientific connotation and mechanism of its effective components. In this study, an ultra‐high performance liquid chromatography coupled with quadrupole time‐of‐flight tandem mass spectrometry method was first developed for characterization of its chemical profile, and a total of 142 chemical components including flavonoids, triterpenoids, alkaloids, coumarins, pungent phytochemicals, and other types were detected, among which 41 components were definitively identified with authentic standards. Furthermore, 14 major representative components were simultaneously quantified by high‐performance liquid chromatography with ultraviolet detector, indicating that the content levels of flavonoids were the most abundant in Wendan decoction. In summary, this study established sensitive and practical methods to systematically characterize chemical profile for the first time and simultaneous quantify representative components of Wendan decoction. These findings above would provide a solid chemical basis for disclosure of potential effective components by further in vivo disposal study, and promote therapeutic mechanism researches of Wendan decoction.

In today’s era of big data with excess information, music is common and everyday, which shows the huge amount of music data. How to obtain one’s favorite music from the massive music database has become a problem, and the emergence of music recommendation systems is also inevitable. In this paper, we take digital piano music as the research object, form comprehensive features using spectrum and notes, design classification methods using convolutional neural networks, and further process the classification results and design recommendation algorithms. The basic method of music recommendation of this algorithm is to determine the structure of the network model, determine the corresponding training model, and improve the parameters on the basis of the typical source network model used in the system experiment. Historical behavior chooses to collect information. Then, it reads the audio data on the system and retrieves it from Mel, which reveals the identity of the music. The classification proposal achieves its goal by denying the similarity between customer preferences and the potential of two musical characteristics. Two recommended methods based on convolutional neural networks are tested in this article. On the whole, the accuracy of the user’s comprehensive feature, recommendation method is higher than the recommendation accuracy rate of the multicategory user. In the comparison experiment of the single-category and multicategory recommendation methods, the average accuracy rate of single-category user feature recommendation is 50.35%; and the recommendation accuracy rate of multicategory user features is higher than the recommendation accuracy rate of single-category user features. The experimental results show that the two recommendation methods can achieve better recommendation results.

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