Comparative antioxidant activity and untargeted metabolomic analyses of cherry extracts of two Chinese cherry species based on UPLC-QTOF/MS and machine learning algorithms.

P. pseudocerasus and P. tomentosa are the two native Chinese cherry species of high economic and ornamental worths. Little is known about the metabolic information of P. pseudocerasus and P. tomentosa. Effective means are lacking for distinguishing these two similar species. In this study, the differences in total phenolic content (TPC), total flavonoid content (TFC), and in vitro antioxidant activities in 21 batches of two species of cherries were compared. A comparative UPLC-QTOF/MS-based metabolomics coupled with three machine learning algorithms was established for differentiating the cherry species. The results demonstrated that P. tomentosa had higher TPC and TFC with average content differences of 12.07 times and 39.30 times, respectively, and depicted better antioxidant activity. Total of 104 differential compounds were identified by UPLC-QTOF/MS metabolomics. The major differential compounds were flavonoids, organooxygen compounds, and cinnamic acids and derivatives. Correlation analysis revealed differences in flavonoids content such as procyanidin B1 or isomer and (Epi)catechin. They could be responsible for differences in antioxidant activities between the two species. Among three machine learning algorithms, the prediction accuracy of support vector machine (SVM) was 85.7%, and those of random forest (RF) and back propagation neural network (BPNN) were 100%. BPNN exhibited better classification performance and higher prediction rate for all testing set samples than those of RF. The study herein found that P. tomentosa had higher nutritional value and biological functions, and thus considered for usage in health products. Machine models based on untargeted metabolomics can be effective tools for distinguishing these two species.

Combined with UPLC-Triple-TOF/MS-based plasma lipidomics and molecular pharmacology reveals the mechanisms of schisandrin against Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD), a type of neurodegeneration disease, is characterized by Aß deposition and tangles of nerve fibers. Schisandrin is one of the main components of Fructus Schisandrae Chinensis. Researches showed that schisandrin can improve the cognitive impairment and memory of AD mice, but the specific mechanism has not been fully elucidated. PURPOSE: The purpose of this study is to investigate the possible mechanism of schisandrin in improving AD pathology. METHODS: The Morris water maze test was executed to detect spatial learning and memory. Ultra performance liquid chromatography-Triple time of flight mass spectrometry (UPLC-Triple-TOF/MS)-based plasma lipidomics was used to study the changes of plasma lipids. Moreover, we measured the levels of protein and mRNA expression of APOE and ABCA1 in the rat brains and in BV2 microglia. RESULTS: Our study found that schisandrin could improve learning and memory, and reduce Aß deposition in AD rats. Furthermore, we found that schisandrin can improve plasma lipid metabolism disorders. Therefore, we hypothesized schisandrin might act via LXR and the docking results showed that schisandrin interacts with LXRß. Further, we found schisandrin increased the protein and mRNA expression of LXR target genes APOE and ABCA1 in the brain of AD rats and in BV2 microglia. CONCLUSION: Our study reveals the neuroprotective effect and mechanism of schisandrin improves AD pathology by activating LXR to produce APOE and ABCA1.

Schisandrin alleviates the cognitive impairment in rats with Alzheimer's disease by altering the gut microbiota composition to modulate the levels of endogenous metabolites in the plasma, brain, and feces.

Schisandrin is one of the main active compounds isolated from the fruit of Schisandrae chinensis Fructus, which is scientifically proven to have beneficial effects on Alzheimer's disease (AD) treatment at the cellular and whole organism level. However, the oral availability of schisandrin is very low, thus implying that the underlying mechanism of therapeutic effect on AD treatment is yet to be clarified fully. Therefore, we speculated that the therapeutic effect of schisandrin on AD is mainly by regulating the imbalance of the gut microbiota (GM). In this study, behavioral experiments and H&E staining were used to confirm the pharmacological effects of schisandrin on rats with AD. 16S rDNA gene sequencing and feces, plasma, and brain metabolomics techniques were utilized to investigate the therapeutic effects and the underlying mechanisms of schisandrin on cognitive impairment in rats with AD. The results indicated that schisandrin improved cognitive impairment and hippocampal cell loss in rats. The UPLC-QTOF/MS-based metabolomics studies of the feces, plasma, and brain revealed that 44, 96, and 40 potential biomarkers, respectively, were involved in the treatment mechanism of schisandrin. Schisandrin improved the metabolic imbalance in rats with AD, and the metabolic changes mainly affected the primary bile acid biosynthesis, sphingolipid metabolism, glycerophospholipid metabolism, and unsaturated fatty acid biosynthesis. Schisandrin can improve the GM structure disorder and increase the abundance of beneficial bacteria in the gut of rats with AD. The predictive metagenomics analysis indicated that the altered GM was mainly involved in lipid metabolism, steroid hormone biosynthesis, arachidonic acid metabolism, biosynthesis of unsaturated fatty acids, and bacterial invasion of epithelial cells. Spearman's correlation analysis showed a significant correlation between affected bacteria and metabolites in various metabolic pathways. Overall, the data underline that schisandrin improves the cognitive impairment in rats with AD by affecting the composition of the GM community, thus suggesting the potential therapeutic effect of schisandrin on the brain-gut axis in rats with AD at the metabolic level.

Mechanical Properties of Macromolecular Separators for Lithium-Ion Batteries Based on Nanoindentation Experiment.

High tensile strength and toughness play an important role in improving the mechanical performance of separator films, such as resistance to external force, improving service life, etc. In this study, a nanoindentation experiment is performed to investigate the mechanical properties of two types of separators for LIBs based on the grid nanoindentation method. During the indentation experiment, the "sink-in" phenomenon is observed around the indenter when plastic deformation of the specimen occurs. The "sink-in" area of the polyethylene (PE) separator is larger than that of the polypropylene/polyethylene/polypropylene (PP/PE/PP) separator, i.e., the plastic area of the PE separator is larger than that of the PP/PE/PP separator. In order to select a suitable method to evaluate the hardness and elastic modulus of these separators for LIBs, three theoretical methods, including the Oliver-Pharr method, the indentation work method, and the fitting curve method, are used for analysis and comparison in this study. The results obtained by the fitting curve method are more reasonable and accurate, which not only avoids the problem of the large contact area obtained by the Oliver-Pharr method, but also avoids the influence caused by the large fitting data of the displacement-force curve and the inaccuracy of using the maximum displacement obtained by the indentation method. In addition, the obstruction ability of the PP/PE/PP separator to locally resist external load pressed into its surface and to resist micro particles, such as fine metal powder, that can enter the lithium-ion battery during the manufacturing process is greater than that of the PE separator. This research provides guidance for studying the mechanical properties and exploring the estimation method of macromolecular separators for LIBs.

Explore the Therapeutic Composition and Mechanism of Schisandra chinensis-Acorus tatarinowii Schott on Alzheimer's Disease by Using an Integrated Approach on Chemical Profile, Network Pharmacology, and UPLC-QTOF/MS-Based Metabolomics Analysis.

Background: Alzheimer's disease places a heavy economic burden to healthcare systems around the world. However, the effective treatments are still lacking. Traditional Chinese medicines (TCM) of Schisandra chinensis and Acorus tatarinowii Schott have the pharmacological effects of sedation and neuroprotection and have been clinically proven to be effective in the treatment of AD. However, their main anti-Alzheimer's compounds and functional mechanisms remain unclear. Purpose: To elucidate the main therapeutic components and possible mechanisms of Sc-At in AD using a comprehensive strategy combining metabolomics and network pharmacology. Methods: First, the UPLC-QTOF/MS method was used to identify the main chemical constituents of Schisandra chinensis and Acorus tatarinowii Schott alcohol extracts in vitro and in vivo. Secondly, the theoretical active ingredients, targets, and pathways of Sc-At for AD treatment were predicted by network pharmacology methods. Finally, plasma metabolomics were detected by UPLC-QTOF/MS to analyze the differential metabolites and metabolic pathways related to Sc-At. Based on the analyses above, the anti-AD mechanism of Sc-At was explored. Results: A total of 95 chemical components were identified in Sc-At extracts in vitro, and 34 prototype drug components were detected in rat plasma; network pharmacology screening identified 14 drug components in line with the principle of Lipinski, of which 10 were present for in vitro drug composition analysis. For these 10 components, 58 AD disease targets were predicted, and 85 AD-related KEGG signaling pathways were enriched. Six core biomarkers of Sc-At (cis-8,11,14,17-eicosatetraenoic acid, prostaglandin H2, sphingosine 1-phosphate, enol-phenylpyruvate, 3-methoxytyrosine, and pristanoyl-CoA) were regulated to a normal state during the treatment of AD. Conclusion: The mechanism of Sc-At for the treatment of AD can be achieved by the effect of the 10 compounds of Sc-At on TNF, MAPK8, MAPK14, PTGS1, and other targets, thereby affecting arachidonic acid metabolism, neurotransmitters, and sphingolipid metabolism.

Develop a stepwise integrated method to screen biomarkers of Baihe-Dihuang Tang on the treatment of depression in rats applying with composition screened, untargeted, and targeted metabolomics analysis.

Baihe-Dihuang Tang is a commonly prescribed remedy for depression. In this study, component screening with untargeted and targeted metabolomics was used to identify potential biomarkers for depression in chronic unpredictable mildly stressed rats. Using this novel identification method, the screening of organic acids, lily saponins, iridoids, and other ingredients formed the basis for subsequent metabolomics research. Baihe-Dihuang Tang supplementation in chronic unpredictable mild-stress-induced depression models, increased their body weight, sucrose preference, brain-derived neurotrophic factor deposition, and spatial exploring. Untargeted metabolomics revealed that Baihe-Dihuang Tang exerts its antidepressant effects by regulating the levels of lipids, organic acids, and its derivatives, and benzenoids in the brain, plasma, and urine of the depressed rats. Moreover, it also modulates the d-glutamine and d-glutamate metabolism and purine metabolism. Targeted metabolomics demonstrated significant reduction in l-glutamate levels in the brains of depressed rats. This could be a potential biomarker for depression. Baihe-Dihuang Tang alleviated depression by regulating the levels of l-glutamate, xanthine, and adenine in the brains of depressed rats. Together, these findings conclusively established the promising therapeutic effect of Baihe-Dihuang Tang on depression and also unraveled the underlying molecular mechanism of its potential antidepressant function.

Self-Polymerized Dopamine Nanoparticles Modified Separators for Improving Electrochemical Performance and Enhancing Mechanical Strength of Lithium-Ion Batteries.

Separators in lithium-ion batteries (LIBs) play an important role for battery safety, so stable electrochemical performance and high mechanical strength of separators will always be of interest. On the basis of the fact that polydopamine (PDA) nanoparticles found in mussel have a strong adhesion ability, biomaterial surface nanoparticles modification methods are developed to increase electrochemical performance and enhance mechanical strength of polypropylene (PP) and polypropylene/polyethylene/polypropylene (PP/PE/PP) separators. The electrolyte uptake performance, ionic conductivities, discharging rate capabilities, yield stresses, and failure strains of PP and PP/PE/PP separators are all enhanced remarkably by PDA modification. Thermal shrinkage results show that thermal stabilities and the shrinkage percentage of PDA-modified separators are improved. The electrochemical testing results conclude that the discharging capacities of PP (increased by 3.77%~187.57%) and PP/PE/PP (increased by 2.31%~92.21%) separators increase remarkably from 0.1 C to 5.0 C. The ionic conductivities of PDA-modified PP and PP/PE/PP separators are 1.5 times and 6.1 times higher than that of unmodified PP and PP/PE/PP separators, which in turn increase the electrolyte uptake and ionic migration. In addition, mechanical properties of PP (yield stresses: 17.48%~100.11%; failure stresses: 13.45%~82.71%; failure strains: 4.08%~303.13%) and PP/PE/PP (yield stresses: 11.77%~296.00%; failure stresses: 12.50%~248.30%; failure strains: 16.53%~32.56%) separators are increased greatly.

An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection.

This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.