Study of Xuanhuang Pill in protecting against alcohol liver disease using ultra-performance liquid chromatography/time-of-flight mass spectrometry and network pharmacology

IntroductionXuanhuang Pill (XHP) is a traditional Chinese medicine oral formula composed of 10 herbs. This study aims to verify the hepatoprotective activity of XHP and explain its possible mechanism.MethodsThe hepatoprotective activity of XHP was evaluated by constructing a mouse model of alcoholic liver disease, and the mechanism of XHP was preliminarily explained by utilizing ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC-QTOF/MS), proteomics and network pharmacology.ResultsThe current study demonstrated that treatment with XHP ameliorated acute alcohol-induced liver injury in mice by significantly reducing alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels and triglycerides (TGs) and malondialdehyde (MDA) content. Remarkably, treatment also increased superoxide dismutase (SOD) activity and glutathione (GSH) content. UPLC-QTOF/MS, 199 compounds were identified as within the make-up of the XHP. Network pharmacology analysis showed that 103 targets regulated by 163 chemical components may play an important role in the protective liver effect mediated by XHP. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis suggest that the HIF-1, FoxO, PI3K-Akt, insulin, and thyroid hormone signaling pathways are key modulators of XHP’s effects. Finally, eight key targets including Mapk1, Mapk3, Akt1, Map2k1, Pik3ca, Pik3cg, Raf1, and Prkca were verified by molecular docking and proteomics analysis, which provide insight into the hepatoprotective effect observed with XHP treatment.ConclusionIn summary, these results improved upon knowledge of the chemical composition and the potential mechanisms of hepatoprotective action of oral XHP treatment, providing foundational support for this formulation as a viable therapeutic option for alcoholic liver disease

Carbonation Curing on Magnetically Separated Steel Slag for the Preparation of Artificial Reefs

Magnetic separation is an effective method to recover iron from steel slag. However, the ultra-fine tailings generated from steel slag become a new issue for utilization. The dry separation processes generates steel slag powder, which has hydration activity and can be used as cement filler. However, wet separation processes produce steel slag mud, which has lost its hydration activity and is no longer suitable to be used as a cement filler. This study investigates the potential of magnetically separated steel slag for carbonation curing and the potential use of the carbonated products as an artificial reef. Steel slag powder and steel slag mud were moulded, carbonation-cured and seawater-cured. Various testing methods were used to characterize the macro and micro properties of the materials. The results obtained show that carbonation and hydration collaborated during the carbonation curing process of steel slag powder, while only carbonation happened during the carbonation curing process of steel slag mud. The seawater-curing process of carbonated steel slag powder compact had three stages: C-S-H gel formation, C-S-H gel decomposition and equilibrium, which were in correspondence to the compressive strength of compact increasing, decreasing and unchanged. However, the seawater-curing process of carbonated steel slag mud compact suffered three stages: C-S-H gel decomposition, calcite transfer to vaterite and equilibrium, which made the compressive strength of compact decreased, increased and unchanged. Carbonated steel slags tailings after magnetic separation underwent their lowest compressive strength when seawater-cured for 7 days. The amount of CaO in the carbonation active minerals in the steel slag determined the carbonation consolidation ability of steel slag and durability of the carbonated steel slag compacts. This paper provides a reference for preparation of artificial reefs and marine coagulation materials by the carbonation curing of steel slag

Proteomics and network pharmacology of Ganshu Nuodan capsules in the prevention of alcoholic liver disease

IntroductionGanshu Nuodan is a liver-protecting dietary supplement composed of Ganoderma lucidum (G. lucidum) spore powder, Pueraria montana (Lour.) Merr. (P. montana), Salvia miltiorrhiza Bunge (S. miltiorrhiza) and Astragalus membranaceus (Fisch.) Bunge. (A. membranaceus). However, its pharmacodynamic material basis and mechanism of action remain unknown.MethodsA mouse model of acute alcohol liver disease (ALD) induced by intragastric administration of 50% alcohol was used to evaluate the hepatoprotective effect of Ganshu Nuodan. The chemical constituents of Ganshu Nuodan were comprehensively identified by UPLC-QTOF/MS, and then its pharmacodynamic material basis and potential mechanism of action were explored by proteomics and network pharmacology.ResultsGanshu Nuodan could ameliorate acute ALD, which is mainly manifested in the significant reduction of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in serum and malondialdehyde (MDA) content in liver and the remarkably increase of glutathione (GSH) content and superoxide dismutase (SOD) activity in liver. Totally 76 chemical constituents were identified from Ganshu Nuodan by UPLC-QTOF/MS, including 21 quinones, 18 flavonoids, 11 organic acids, 7 terpenoids, 5 ketones, 4 sterols, 3 coumarins and 7 others. Three key signaling pathways were identified via proteomics studies, namely Arachidonic acid metabolism, Retinol metabolism, and HIF-1 signaling pathway respectively. Combined with network pharmacology and molecular docking, six key targets were subsequently obtained, including Ephx2, Lta4h, Map2k1, Stat3, Mtor and Dgat1. Finally, these six key targets and their related components were verified by molecular docking, which could explain the material basis of the hepatoprotective effect of Ganshu Nuodan.ConclusionGanshu Nuodan can protect acute alcohol-induced liver injury in mice by inhibiting oxidative stress, lipid accumulation and apoptosis. Our study provides a scientific basis for the hepatoprotective effect of Ganshu Nuodan in acute ALD mice and supports its traditional application

Evaluation of Virus-Free Chrysanthemum ‘Hangju’ Productivity and Response to Virus Reinfection in the Field: Molecular Insights into Virus–Host Interactions

The shoot apical meristem culture has been used widely to produce virus-free plantlets which have the advantages of strong disease resistance, high yield, and prosperous growth potential. However, this virus-free plant will be naturally reinfected in the field. The physiological and metabolic responses in the reinfected plant are still unknown. The flower of chrysanthemum ‘Hangju’ is a traditional medicine which is unique to China. In this study, we found that the virus-free ‘Hangju’ (VFH) was reinfected with chrysanthemum virus B/R in the field. However, the reinfected VFH (RVFH) exhibited an increased yield and medicinal components compared with virus-infected ‘Hangju’ (VIH). Comparative analysis of transcriptomes was performed to explore the molecular response mechanisms of the RVFH to CVB infection. A total of 6223 differentially expressed genes (DEGs) were identified in the RVFH vs. the VIH. KEGG enrichment and physiological analyses indicated that treatment with the virus-free technology significantly mitigated the plants’ lipid and galactose metabolic stress responses in the RVFH. Furthermore, GO enrichment showed that plant viral diseases affected salicylic acid (SA)-related processes in the RVFH. Specifically, we found that phenylalanine ammonia-lyase (PAL) genes played a major role in defense-related SA biosynthesis in ‘Hangju’. These findings provided new insights into the molecular mechanisms underlying plant virus–host interactions and have implications for developing strategies to improve plant resistance against viruses

How to Determine the Early Warning Threshold Value of Meteorological Factors on Influenza through Big Data Analysis and Machine Learning

Infectious diseases are a major health challenge for the worldwide population. Since their rapid spread can cause great distress to the real world, in addition to taking appropriate measures to curb the spread of infectious diseases in the event of an outbreak, proper prediction and early warning before the outbreak of the threat of infectious diseases can provide an important basis for early and reasonable response by the government health sector, reduce morbidity and mortality, and greatly reduce national losses. However, if only traditional medical data is involved, it may be too late or too difficult to implement prediction and early warning of an infectious outbreak. Recently, medical big data has become a research hotspot and has played an increasingly important role in public health, precision medicine, and disease prediction. In this paper, we focus on exploring a prediction and early warning method for influenza with the help of medical big data. It is well known that meteorological conditions have an influence on influenza outbreaks. So, we try to find a way to determine the early warning threshold value of influenza outbreaks through big data analysis concerning meteorological factors. Results show that, based on analysis of meteorological conditions combined with influenza outbreak history data, the early warning threshold of influenza outbreaks could be established with reasonable high accuracy

Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and Early-Warning Model

Accompanied by the rapid economic and social development, there is a phenomenon of the crazy spread of many infectious diseases. It has brought the rapid growth of the number of people infected with hand-foot-and-mouth disease (HFMD), and children, especially infants and young children’s health is at great risk. So it is very important to predict the number of HFMD infections and realize the regional early-warning of HFMD based on big data. However, in the current field of infectious diseases, the research on the prevalence of HFMD mainly predicts the number of future cases based on the number of historical cases in various places, and the influence of many related factors that affect the prevalence of HFMD is ignored. The current early-warning research of HFMD mainly uses direct case report, which uses statistical methods in time and space to have early-warnings of outbreaks separately. It leads to a high error rate and low confidence in the early-warning results. This paper uses machine learning methods to establish a HFMD epidemic prediction model and explore constructing a variety of early-warning models. By comparison of experimental results, we finally verify that the HFMD prediction algorithm proposed in this paper has higher accuracy. At the same time, the early-warning algorithm based on the comparison of threshold has good results

Prioritizing sustainable development goals in China based on a comprehensive assessment accounting for indicator interlinkages

Prioritizing areas and targets, coordinated with development gaps, is necessary to achieve the sustainable development goals (SDGs) in the face of resource limitations resulting from coronavirus disease 2019 (COVID-19). The SDG interlinkages further exacerbate the difficulty inherent in addressing these goals. However, previous studies failed to consider the indicator interlinkages in the process of aggregate performance assessments and thus cannot identify priorities based on indicator interlinkages and development gaps. This study integrated network analysis and the technique for order of preference by similarity to ideal solution (TOPSIS) method to convert indicator interlinkages into indicator weights and then assessed the aggregate performance of the SDGs. Subsequently, the prioritized provinces and indicators were identified at the subnational level of China based on the aggregate performance of the SDGs, SDG growth rate and indicator interlinkages. The per capita net income of rural residents (2.3.2), the proportion of people living below 50% of the median income (10.2.1), and the under-five mortality rate (3.2.1) were the most contributing indicators to the aggregate performance of the SDGs, while the common challenges dealt with SDG 16, SDG 6, SDG 7 and SDG 12. Moreover, accelerating the development of western provinces would make it possible to overcome the traditional imbalance status, while resource-driven provinces should be paid special attention due to their poor aggregate performance of the SDGs and their lower growth rate. Thus, a coordinating strategy is highly recommended for allocating resources to the priority targets and finally achieving the SDGs

The Prediction of Hepatitis E through Ensemble Learning

According to the World Health Organization, about 20 million people are infected with Hepatitis E every year. In 2015, there were 44,000 deaths due to HEV infection worldwide. Food, water and climate are key factors that affect the outbreak of Hepatitis E. This paper presents an ensemble learning model for Hepatitis E prediction by studying the correlation between historical epidemic cases of hepatitis E and environmental factors (water quality and meteorological data). Environmental factors include many features, and ones that are most relevant to HEV are selected and input into the ensemble learning model composed by Gradient Boosting Decision Tree (GBDT) and Random Forest for training and prediction. Three indicators, root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), are used to evaluate the effectiveness of the ensemble learning model against the classical time series prediction model. It is concluded that the ensemble learning model has a better prediction effect than the classical model, and the prediction effectiveness can be improved by exploiting water quality and meteorological factors (radiation, air pressure, precipitation)

Sch9 regulates intracellular protein ubiquitination by controlling stress responses

Protein ubiquitination and the subsequent degradation are important means by which aberrant proteins are removed from cells, a key requirement for long-term survival. In this study, we found that the overall level of ubiquitinated proteins dramatically decreased as yeast cell grew from log to stationary phase. Deletion of SCH9, a gene encoding a key protein kinase for longevity control, decreased the level of ubiquitinated proteins in log phase and this effect could be reversed by restoring Sch9 function. We demonstrate here that the decrease of ubiquitinated proteins in sch9Δ cells in log phase is not caused by changes in ubiquitin expression, proteasome activity, or autophagy, but by enhanced expression of stress response factors and a decreased level of oxidative stress. Our results revealed for the first time how Sch9 regulates the level of ubiquitinated proteins and provides new insight into how Sch9 controls longevity