Identification of the Metabolic Enzyme Involved Morusin Metabolism and Characterization of Its Metabolites by Ultraperformance Liquid Chromatogaphy Quadrupole Time-of-Flight Mass Spectrometry (UPLC/Q-TOF-MS/MS)

Morusin, the important active component of a traditional Chinese medicine, Morus alba L., has been shown to exhibit many vital pharmacological activities. In this study, six recombinant CYP450 supersomes and liver microsomes were used to perform metabolic studies. Chemical inhibition studies and screening assays with recombinant human cytochrome P450s were also used to characterize the CYP450 isoforms involved in morusin metabolism. The morusin metabolites identified varied greatly among different species. Eight metabolites of morusin were detected in the liver microsomes from pigs (PLMs), rats (RLMs), and monkeys (MLMs) by LC-MS/MS and six metabolites were detected in the liver microsomes from humans (HLMs), rabbits (RAMs), and dogs (DLMs). Four metabolites (M1, M2, M5, and M7) were found in all species and hydroxylation was the major metabolic transformation. CYP1A2, CYP2C9, CYP2D6, CYP2E1, CYP3A4, and CYP2C19 contributed differently to the metabolism of morusin. Compared to other CYP450 isoforms, CYP3A4 played the most significant role in the metabolism of morusin in human liver microsomes. These results are significant to better understand the metabolic behaviors of morusin among various species

THE SYNCHRONIZATION SYSTEM BETWEEN RF WAVE AND THE LASER PULSE IN THE PHOTOINJECTOR

Abstract: The synchronization between RF wave and the laser pulse is one of the key questions in photoinjector, it effects the stability and reliability of injector. We design a sampling phase-locked electronic circuit which compare the phases of RF wave and the 28 th harmonic of laser pulse .It enhances the synchronization precision

HLA-DRB1 May Be Antagonistically Regulated by the Coordinately Evolved Promoter and 3′-UTR under Stabilizing Selection

HLA-DRB1 is the most polymorphic MHC (major histocompatibility complex) class II gene in human, and plays a crucial role in the development and function of the immune system. Extensive polymorphisms exist in the promoter and 3′-UTR of HLA-DRB1, especially a LTR (Long terminal repeat) element in the promoter, which may be involved in the expression regulation. However, it remains unknown how the polymorphisms in the whole promoter region and 3′-UTR to regulate the gene expression. In this study, we investigated the extensive polymorphisms in the HLA-DRB1 promoter and 3′-UTR, and how these polymorphisms affect the gene expression in both independent and jointly manners. It was observed that most of the haplotypes in the DRB1 promoter and 3′-UTR were clustered into 4 conserved lineages (H1, H2, H3 and H4), and showed high linkage disequilibrium. Compared with H1 and H2 lineage, a LTR element in the promoter of H3 and H4 lineage significantly suppressed the promoter activity, whereas the activity of the linked 3′-UTR increased, leading to no apparent difference in the final expression product between H1/H2 and H3/H4 lineage. Nevertheless, compared with the plasmid with a promoter and 3′-UTR from the same lineage, the recombinant plasmid with a promoter from H2 and a 3′-UTR from H3 showed about double fold increased luciferase activity, Conversely, the recombinant plasmid with a promoter from H3 and a 3′-UTR from H2 resulted in about 2-fold decreased luciferase activity. These results indicate that the promoter and 3′-UTR of HLA-DRB1 may antagonistically regulate the gene expression, which may be subjected to stabilizing selection. These findings may provide a novel insight into the mechanisms of the diseases associated with HLA-DRB1 genes

Comprehensive comparative analysis of the prognostic impact of systemic inflammation biomarkers for patients underwent cardiac surgery

BackgroundInflammation plays an integral role in the development of cardiovascular disease, and few studies have identified different biomarkers to predict the prognosis of cardiac surgery. But there is a lack of reliable and valid evidence to determine the optimal systemic inflammatory biomarkers to predict prognosis.MethodsFrom December 2015 and March 2021, we collected 10 systemic inflammation biomarkers among 820 patients who underwent cardiac surgery. Time-dependent receiver operating characteristic curves (ROC) curve at different time points and C-index was compared at different time points. Kaplan–Meier method was performed to analyze overall survival (OS). Cox proportional hazard regression analyses were used to assess independent risk factors for OS. A random internal validation was conducted to confirm the effectiveness of the biomarkers.ResultsThe area under the ROC of lymphocyte-to-C-reactive protein ratio (LCR) was 0.655, 0.620 and 0.613 at 1-, 2- and 3-year respectively, and C-index of LCR for OS after cardiac surgery was 0.611, suggesting that LCR may serve as a favorable indicator for predicting the prognosis of cardiac surgery. Patients with low LCR had a higher risk of postoperative complications. Besides, Cox proportional hazard regression analyses indicated that LCR was considered as an independent risk factor of OS after cardiac surgery.ConclusionLCR shows promise as a noteworthy representative among the systemic inflammation biomarkers in predicting the prognosis of cardiac surgery. Screening for low LCR levels may help surgeons identify high-risk patients and guide perioperative management strategies

Development and research trends of a polypropylene material in electrical engineering: A bibliometric mapping analysis and systematical review

In order to explore the development and research trends of polypropylene (PP) in electrical engineering, the research literature is quantitatively analyzed using a bibliometric method with the VOSviewer and CiteSpace software. First, the research literature about PP material in electrical engineering applications is collected, from 1990 to 2022. Then, by analyzing the keyword co-occurrence, keyword co-occurrence timezone, author cooperation network, and national cooperation network, the research hotspots of the PP field and its time evolutionary path and development direction are introduced. It is found that the nano-modification, mechanical, and electrical properties are the most popular research hotspots in this field. Most research studies were completed by few specific researchers. A stable cooperative group has not been formed in this field yet, indicating the necessity of further integration. Most articles about PP were published in dielectric and material journals. It is suggested that more open access journals are required to popularize the existing research results among the public and to promote the development of PP. Although the most published country is China, the United States publishes the most cited papers on average

Optimal Filters with Multiple Packet Losses and its Application in Wireless Sensor Networks

This paper is concerned with the filtering problem for both discrete-time stochastic linear (DTSL) systems and discrete-time stochastic nonlinear (DTSN) systems. In DTSL systems, an linear optimal filter with multiple packet losses is designed based on the orthogonal principle analysis approach over unreliable wireless sensor networks (WSNs), and the experience result verifies feasibility and effectiveness of the proposed linear filter; in DTSN systems, an extended minimum variance filter with multiple packet losses is derived, and the filter is extended to the nonlinear case by the first order Taylor series approximation, which is successfully applied to unreliable WSNs. An application example is given and the corresponding simulation results show that, compared with extended Kalman filter (EKF), the proposed extended minimum variance filter is feasible and effective in WSNs

Endogenous L-Carnosine Level in Diabetes Rat Cardiac Muscle

A novel method for quantitation of cardiac muscle carnosine levels using HPLC-UV is described. In this simple and reliable method, carnosine from the rat cardiac muscle and the internal standard, thymopentin, were extracted by protein precipitation with acetonitrile. The method was linear up to 60.96 μg·mL−1 for L-carnosine. The calibration curve was linear in concentration ranges from 0.5 to 60.96 μg·mL−1. The relative standard deviations obtained for intra- and interday precision were lower than 12% and the recoveries were higher than 90% for both carnosine and internal standard. We successfully applied this method to the analysis of endogenous carnosine in cardiac muscle of the diabetes rats and healthy control rats. The concentration of carnosine was significantly lower in the diabetes rats group, compared to that in the healthy control rats. These results support the usefulness of this method as a means of quantitating carnosine and illustrate the important role of L-carnosine in cardiac muscle

Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder

IntroductionMajor depressive disorder (MDD) is a prevalent mental illness, with severe symptoms that can significantly impair daily routines, social interactions, and professional pursuits. Recently, imaging genetics has received considerable attention for understanding the pathogenesis of human brain disorders. However, identifying and discovering the imaging genetic patterns between genetic variations, such as single nucleotide polymorphisms (SNPs), and brain imaging data still present an arduous challenge. Most of the existing MDD research focuses on single-modality brain imaging data and neglects the complex structure of brain imaging data.MethodsIn this study, we present a novel association analysis model based on a self-expressive network to identify and discover imaging genetics patterns between SNPs and multi-modality imaging data. Specifically, we first build the multi-modality phenotype network, which comprises voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. Then, we apply intra-class similarity information to construct self-expressive networks of multi-modality phenotype features via sparse representation. Subsequently, we design a fusion method guided by diagnosis information, which iteratively fuses the self-expressive networks of multi-modality phenotype features into a single new network. Finally, we propose an association analysis between MDD risk SNPs and the multi-modality phenotype network based on a fusion self-expressive network.ResultsExperimental results show that our method not only enhances the association between MDD risk SNP rs1799913 and the multi-modality phenotype network but also identifies some consistent and stable regions of interest (ROIs) multi-modality biological markers to guide the interpretation of MDD pathogenesis. Moreover, 15 new potential risk SNPs highly associated with MDD are discovered, which can further help interpret the MDD genetic mechanism.DiscussionIn this study, we discussed the discriminant and convergence performance of the fusion self-expressive network, parameters, and atlas selection