Combining network pharmacology with chromatographic fingerprinting and multicomponent quantitative analysis for the quality evaluation of Astragali Radix.

Nowadays, cultivated variants and adulterants of Astragali Radix (AR) have flooded the market, causing the quality assurance of AR to be challenging. To address this issue, we combined network pharmacology with chromatographic fingerprinting and multicomponent quantitative analysis for the quality evaluation of AR. Specifically, through network pharmacology, a complete understanding of the active components and pharmacological activities of AR was established. In addition, establishing fingerprint profiles and multicomponent quantitation by high-performance liquid chromatography (HPLC) is convenient and comprehensive, and can more fully reflect the overall situation of the distribution of various chemical components. To evaluate and differentiate AR from different origins, hierarchical cluster analysis and principal component analysis were performed. The result showed that AR acts synergistically through multiple targets and pathways. The content of chemical components in AR from different origins varied significantly. Combining network pharmacology and multicomponent quantification results, astragaloside II and IV and formononetin can be used as quality markers for the quality control of AR. This study provides a comprehensive and reliable strategy for the quality evaluation of AR and identifies its quality markers to ensure the quality of the herb.

[Research progress about different levels of cognitive recession using resting state functional connectivity network methods].

Normal brain aging and a serious of neurodegenerative diseases may lead to decline in memory, attention and executive ability and poorer quality of life. The mechanism of the decline is not clear now and is still a hot issue in the fields of neuroscience and medicine. A large number of researches showed that resting state functional brain networks based functional magnetic resonance imaging (fMRI) are sensitive and susceptive to the change of cognitive function. In this paper, the researches of brain functional connectivity based on resting fMRI in recent years were compared, and the results of subjects with different levels of cognitive decline including normal brain aging, mild cognitive impairment (MCI) and Alzheimer's disease (AD) were reviewed. And the changes of brain functional networks under three different levels of cognitive decline are introduced in this paper, which will provide the basis for the detection of normal brain aging and clinical diseases.

[Research on the characteristics of the dynamic functional connectivity network related to aging].

Brain aging can affect the strength of functional connectivity between brain regions. In recent years, studies have shown that functional connectivity is fluctuant over time, and can reflect more physiological and pathological information. Therefore, in the study resting state functional magnetic resonance imaging (fMRI) data of 32 elderly subjects and 36 younger subjects were selected, and the sliding window technique was used to estimate dynamic functional connectivity network. Then, the dependency of fluctuating energy difference on frequency band was studied using wavelet packet analysis, conducting the linear regression with age at the same time. Results showed that the fluctuating energy in older group was significantly higher than that in the young group in low frequency, and it was significantly lower than that in the young people in high frequency. These results suggested that the dynamic functional connectivity between networks in the elderly exist slow wave phenomenon, which may be related to the decreased reaction rate of the elderly. This article provides new ideas and methods for the research about brain aging, and promotes a theoretical basis for further understanding of the physiological significance of brain dynamic functional connectivity.

Comparative chemical characters of Pseudostellaria heterophylla from geographical origins of China.

Objective: Pseudostellaria heterophylla has been paid more attention in recent years, mainly as a medicine food homology plant. The content determination of P. heterophylla is not specified in the Chinese Pharmacopoeia (version 2020). The environmental conditions in different production areas could exert an influence on the quality of P. heterophylla. The purpose of this study is to discriminate P. heterophylla collected from different geographical origins of China. Methods: In this study, the content of polysaccharide in 28 batches of P. heterophylla was determined using phenol-sulfuric acid. HPLC fingerprints were established under optimised HPLC-PDA methods. Subsequently, the similarity analysis (SA) and the quantification of heterophyllin B were analyzed. The metabolites of P. heterophylla were identified and evaluated using UHPLC-Q Exactive HF orbitrap MS system. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), hierarchical cluster analysis (HCA) and orthogonal PLS-DA (OPLS-DA) were performed based on all peak areas. Results: The polysaccharide content in Guizhou and Jiangsu was higher than that of other production areas, which varied significant from different origins. While the content of heterophyllin B in Anhui and Jiangsu was high. The correlation coefficients of HPLC fingerprints for 28 batches samples ranged from 0.877 to 0.990, and the characteristic map can be used to identify and evaluate the quality of P. heterophylla. The samples from Fujian, Guizhou, Jiangsu provinces can be relatively separated using multivariate statistical analysis including PCA, PLS-DA, HCA, OPLS-DA, indicating that their metabolic compositions were significantly different. Ultimately, a total of 15 metabolites which were filtrated by a VIP-value > 1 and a P-value < 0.05 associated with the separation of different origins were identified. Conclusion: HPLC fingerprint was established to evaluate the quality and authenticity of P. heterophylla. The present work showed that the difference of geographic distributions had an influence on the internal chemical compositions. A sensitive and rapid untargeted metabolomics approach by UHPLC-Q Exactive HF orbitrap MS was utilized to evaluate P. heterophylla from different origins in China for the first time. Overall, this study provides insights to metabolomics of P. heterophylla and supplies important reference values for the development of functional foods.

Prevalence and Genetic Diversity of Cronobacter Species Isolated From Four Infant Formula Production Factories in China.

The aim of this study was to investigate the prevalence and genotypic characteristics of Cronobacter isolated from powdered infant formula (PIF) manufacturing facilities and to identify a potential source of contamination. A total of 42 Cronobacter isolates (5%) were detected in 835 environmental samples collected during the surveillance study. These isolates included C. sakazakii (n = 37), C. malonaticus (n = 3), and C. turicensis (n = 2). The isolates were divided into 14 sequence types (STs) by multi-locus sequence typing (MLST) and 21 pulsotypes (PTs) using pulsed-field gel electrophoresis (PFGE). The dominant C. sakazakii sequence types were ST3 (n = 12) and ST21 (n = 10), followed by ST136 (n = 6). The major PTs were PT22 (n = 12) and PT17 (n = 4) based on 100% similarity. Strains isolated from samples collected at the same production facility showed closer phylogenetic relation than those collected from distinct facilities. The result of extensive traceback sampling showed that PIF residues (PIF dust in production areas), fluid beds, drying areas, floors, and soil samples collected adjacent to the production facilities were the primary positive areas for Cronobacter. The present study outlines an effective approach to determine prevalence and genetic diversity of Cronobacter isolates associated with contamination of PIF.

Automated detection of pathologic white matter alterations in Alzheimer's disease using combined diffusivity and kurtosis method.

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are important diffusion MRI techniques for detecting microstructure abnormities in diseases such as Alzheimer's. The advantages of DKI over DTI have been reported generally; however, the indistinct relationship between diffusivity and kurtosis has not been clearly revealed in clinical settings. In this study, we hypothesize that the combination of diffusivity and kurtosis in DKI improves the capacity of DKI to detect Alzheimer's disease compared with diffusivity or kurtosis alone. Specifically, a support vector machine-based approach was applied to combine diffusivity and kurtosis and to compare different indices datasets. Strict assessments were conducted to ensure the reliability of all classifiers. Then, data from the optimized classifiers were used to detect abnormalities. With the combination, high accuracy performances of 96.23% were obtained in 53 subjects, including 27 Alzheimer's patients. More highly scored abnormal regions were selected by the combination than alone. The results revealed that more precise diffusivity and complementary kurtosis mainly contributed to the high performance of the combination in DKI. This study provides further understanding of DKI and the relationship between diffusivity and kurtosis in pathologic white matter alterations in Alzheimer's disease.

Age-Related Decline in the Variation of Dynamic Functional Connectivity: A Resting State Analysis.

Normal aging is typically characterized by abnormal resting-state functional connectivity (FC), including decreasing connectivity within networks and increasing connectivity between networks, under the assumption that the FC over the scan time was stationary. In fact, the resting-state FC has been shown in recent years to vary over time even within minutes, thus showing the great potential of intrinsic interactions and organization of the brain. In this article, we assumed that the dynamic FC consisted of an intrinsic dynamic balance in the resting brain and was altered with increasing age. Two groups of individuals (N = 36, ages 20-25 for the young group; N = 32, ages 60-85 for the senior group) were recruited from the public data of the Nathan Kline Institute. Phase randomization was first used to examine the reliability of the dynamic FC. Next, the variation in the dynamic FC and the energy ratio of the dynamic FC fluctuations within a higher frequency band were calculated and further checked for differences between groups by non-parametric permutation tests. The results robustly showed modularization of the dynamic FC variation, which declined with aging; moreover, the FC variation of the inter-network connections, which mainly consisted of the frontal-parietal network-associated and occipital-associated connections, decreased. In addition, a higher energy ratio in the higher FC fluctuation frequency band was observed in the senior group, which indicated the frequency interactions in the FC fluctuations. These results highly supported the basis of abnormality and compensation in the aging brain and might provide new insights into both aging and relevant compensatory mechanisms.