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Natural medicines play a crucial role in clinical drug applications, serving as a primary traditional Chinese medicine for the clinical treatment of liver fibrosis. Understanding the in vivo metabolic process of the Fuzheng Huayu (FZHY) formula is essential for delving into its material basis and mechanism. In recent years, there has been a growing body of research focused on the mechanisms and component analysis of FZHY. This study aimed to examine the pharmacokinetics of FZHY in healthy volunteers following oral administration. Blood samples were collected at designated time intervals after the oral intake of 9.6‐g FZHY tablets. A UHPLC‐Q/Exactive method was developed to assess the plasma concentrations of five components post‐FZHY ingestion. The peak time for all components occurred within 10 min. The peak concentration (Cmax) values for amygdalin, schisandrin, and schisandrin A ranged from 3.47 to 28.80 ng/mL, with corresponding AUC(0–t) values ranging from 10.63 to 103.20 ng h/mL. For schisandrin B and prunasin, Cmax values were in the range of 86.52 to 229.10 ng/mL, and their AUC(0–t) values ranged from 375.26 to 1875.54 ng h/mL, indicating significant exposure within the body. These findings demonstrate that the developed method enables rapid and accurate detection and quantification of the five components in FZHY, offering a valuable reference for its clinical study. [ABSTRACT FROM AUTHOR]

Pneumonia refers to a death‐causing infection. Astragali Radix (AR) and Atractylodis Macrocephalae Rhizoma (AMR) are widely used as traditional tonic and promising edible immunomodulatory herbal medicine, but the systemic mechanism is not well understood. Therefore, a strategy based on network pharmacology and molecular docking was designed to explore the systemic mechanism of AR‐AMR acting on pneumonia. After a series of bioinformatics assays, seven kernel targets were obtained, including TNF, IL6, IFNG, IL1B, IL10, IL4, and TLR9. And seven key compounds were identified as the synergy components of AR‐AMR acting on pneumonia, the four key compounds belonging to AR were (3R)‐3‐(2‐hydroxy‐3,4‐dimethoxyphenyl)‐7‐chromanol, formononetin, quercetin, and kaempferol, the three key compounds belonging to AMR were atractylone, 14‐acetyl‐12‐senecioyl‐2E, 8E, 10E‐atractylentriol, and α‐Amyrin. The crucial pathways were mainly related to three modules, including immune diseases, infectious disease, and organismal systems. Collectively, these observations strongly suggest that the molecular mechanisms of AR‐AMR regulating pneumonia were closely related to the correlation between inflammation and immune response. Practical applications: Astragali radix and Atractylodis macrocephalae rhizoma can be used as "medicine‐food homology" for dietary supplement. AR and AMR are widely used as a traditional tonic and promising edible immunomodulatory herbal medicine. The AR‐AMR herb pairs are used for compatibility many times in the recommended prescriptions in COVID‐19 develop pneumonia in China. However, the ingredients and mechanisms of AR‐AMR acting on Pneumonia via immunomodulation are unclear. In this paper, bioinformatics and network biology were used to systematically explore the mechanisms of the AR‐AMR herb pairs in treatment of pneumonia, and further analyze the correlation mechanism between it and COVID‐19 develop pneumonia. To sum up, our study reveals the interrelationships between components, targets, and corresponding biological processes of AR‐AMR acting on pneumonia. Understanding these relationships may provide guidance and theoretical basis for the further application of AR‐AMR herb pairs. [ABSTRACT FROM AUTHOR]

In this paper, we study the uplink in a single-cell massive multiple-input–multiple-output system. The base station (BS) is equipped with three antenna arrays, each covering one third of the cell area. Each antenna array comprises a large yet finite number of antennas. The single-antenna users are randomly and uniformly distributed in the cell, transmitting to the BS utilizing full channel inversion power control. All users experience Rician fading. Receiver maximum-ratio-combining is performed at the BS. Under such a setting, we focus on one cell sector and analyze the intrasector interference in a realistic situation where the number of BS antennas is not extremely large compared with the user number. In particular, we show that, due to the line-of-sight (LoS) component of the channel, the interference is partially determined by the angles of arrival of the signals. We approximate the LoS component interference by a Beta mixture. The interference in Rician fading is then modeled as a noncentral chi-square distribution with a random noncentrality parameter, corresponding to the LoS component. The approximate interference distribution can be used to compute signal-to-interference-ratio-dependent metrics such as outage probability and average throughput. [ABSTRACT FROM AUTHOR]

Triptolide (TP) is the major pharmacologically active ingredient and toxic component of Tripterygium wilfordii Hook. f. However, its clinical potential is limited by a narrow therapeutic window and multiple organ toxicity, especially hepatotoxicity. Furthermore, TP-induced hepatotoxicity shows significant inter-individual variability. Over the past few decades, research has been devoted to the study of TP-induced hepatotoxicity and its mechanism. In this review, we summarized the mechanism of TP-induced hepatotoxicity. Studies have demonstrated that TP-induced hepatotoxicity is associated with CYP450s, P-glycoprotein (P-gp), oxidative stress, excessive autophagy, apoptosis, metabolic disorders, immunity, and the gut microbiota. These new findings provide a comprehensive understanding of TP-induced hepatotoxicity and detoxification., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hu, Wu, Wang, Zhang, Liu, Zhou and Yang.)

Background: Diabetes mellitus is one of the most common endocrine metabolic disorder- related diseases. The application of herbal medicine to control glucose levels and improve insulin action might be a useful approach in the treatment of diabetes. Mulberry leaves (ML) have been reported to exert important activities of anti-diabetic., Objective: In this work, we aimed to explore the multi-targets and multi-pathways regulatory molecular mechanism of Mulberry leaves (ML, Morus alba Linne) acting on diabetes., Methods: Identification of active compounds of Mulberry leaves using Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was carried out. Bioactive components were screened by FAF-Drugs4 website (Free ADME-Tox Filtering Tool). The targets of bioactive components were predicted from SwissTargetPrediction website, and the diabetes related targets were screened from GeneCards database. The common targets of ML and diabetes were used for Gene Ontology (GO) and pathway enrichment analysis. The visualization networks were constructed by Cytoscape 3.7.1 software. The biological networks were constructed to analyze the mechanisms as follows: (1) compound-target network; (2) common target-compound network; (3) common targets protein interaction network; (4) compound-diabetes protein-protein interactions (ppi) network; (5) target-pathway network; and (6) compound-target-pathway network. At last, the prediction results of network pharmacology were verified by molecular docking method., Results: 17 active components were obtained by TCMSP database and FAF-Drugs4 website. 51 potential targets (11 common targets and 40 associated indirect targets) were obtained and used to build the PPI network by the String database. Furthermore, the potential targets were used for GO and pathway enrichment analysis. Eight key active compounds (quercetin, Iristectorigenin A, 4- Prenylresveratrol, Moracin H, Moracin C, Isoramanone, Moracin E and Moracin D) and 8 key targets (AKT1, IGF1R, EIF2AK3, PPARG, AGTR1, PPARA, PTPN1 and PIK3R1) were obtained to play major roles in Mulberry leaf acting on diabetes. And the signal pathways involved in the mechanisms mainly include AMPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, insulin signaling pathway and insulin resistance. The molecular docking results show that the 8 key active compounds have good affinity with the key target of AKT1, and the 5 key targets (IGF1R, EIF2AK3, PPARG, PPARA and PTPN1) have better affinity than AKT1 with the key compound of quercetin., Conclusion: Based on network pharmacology and molecular docking, this study provided an important systematic and visualized basis for further understanding of the synergy mechanism of ML acting on diabetes., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)