雷公藤红素通过靶向核受体Nur77促进损伤线粒体自噬而抑制炎症反应

文章简介线粒体在细胞死亡、自噬、免疫和炎症中起着不可或缺的作用。前期研究发现,孤儿核受体Nur77通过靶向线粒体诱导细胞凋亡。本文报道了Nur77作为具有抗炎作用的雷公藤红素的直接靶点,介导雷公藤红素通过自噬清除损伤线粒体,抑制炎症反应而达到治疗炎症疾病包括肥胖症的功能。研究人员发现,雷公藤红素的结合

Antidepressant Effects of Repetitive Transcranial Magnetic Stimulation Over Prefrontal Cortex of Parkinson's Disease Patients With Depression: A Meta-Analysis

Objective: The purpose of this meta-analysis was to investigate the antidepressant effects of repetitive transcranial magnetic stimulation (rTMS) over the prefrontal cortex (PFC) of patients with Parkinson's disease (PD) and to determine the optimal rTMS parameters, such as the intensity, frequency and the delivered pattern of rTMS stimulation.Methods: EMBASE, PubMed, Web of Science, MEDLINE, and Cochrane data bases were researched for papers published before March 12, 2018. Studies investigating the anti-depression effects of rTMS over PFC in patients with PD were considered. The main outcomes of pre- and post-rTMS treatment as well as score changes were all extracted. The mean effect size was estimated by calculating the standardized mean difference (SMD) with 95% confidence interval (CI) by using fixed or random effect models as appropriate.Results: Nine studies containing 137 PD patients with depression were included. The pooled results showed significant pre-post anti-depressive effects of rTMS over PFC in PD patients with depression (SMD = −0.80, P < 0.00001). The subgroup analyses of stimulation intensity, frequencies, and models also revealed significant effects (Intensities: 90% RMT: SMD = −1.16, P = 0.0006; >100% RMT: SMD = −0.82, P < 0.0001. Frequencies: < 1.0 Hz: SMD = −0.83, P = 0.03; 5.0 Hz: SMD = −1.10, P < 0.0001; ≥10.0 Hz: SMD = −0.55, P = 0.02. Models: Continuous: SMD = −0.79, P < 0.0001; Discontinuous: SMD = −0.84, P = 0.02). But the results of the studies with place-controlled designs were not significant (Overall: SMD = −0.27, P = 0.54. Intensities: 90% RMT: SMD = 0.27, P = 0.68; 100% RMT: SMD = −0.32, P = 0.33. Frequencies: 5.0 Hz: SMD = −0.87, P = 0.10; ≥10.0 Hz: SMD = 0.27, P = 0.66. Models: Continuous: SMD = −0.28, P = 0.68; Discontinuous: SMD = −0.32, P = 0.33). The greater effect sizes of rTMS with 90% RMT, 5.0 Hz in discontinuous days can be observed rather than the other parameters in both kinds of analyses across study design.Conclusions: rTMS may have a significant positive pre-post anti-depressive effect over PFC on patients with depression, especially by using 5.0 Hz frequency with 90% RMT intensity in discontinuous days, which may produce better effects than other parameters. The real effect, though, was not different from that of the placebo. Future studies with larger sample sizes and high-quality studies are needed to further corroborate our results and to identify the optimal rTMS protocols

Bioinformatics-Based Identification of lncRNA-miRNA-mRNA Network in Dilated Cardiomyopathy and Drug Prediction

Background. Dilated cardiomyopathy (DCM) is a cardiovascular disease of unknown etiology with progressive aggravation. More and more studies have shown that long noncoding RNAs (lncRNAs) play an essential role in dilated cardiomyopathy formation and development. The mechanism of action of competitive endogenous RNA (ceRNA) networks formed based on the principle that lncRNAs affect mRNAs’ expression level by competitively binding microRNAs (miRNAs) in dilated cardiomyopathy has rarely been reported. Objective. This study is aimed at constructing a lncRNA-miRNA-mRNA ceRNA network by bioinformatics analysis methods, discovering, and validating potential biomarkers of DCM in the ceRNA network and determining possible therapeutic targets from them for drug prediction. Methods. A lncRNA dataset and a mRNA microarray dataset were downloaded from the Gene Expression Omnibus Database (GEO). Gene expression was compared between blood samples from patients with dilated cardiomyopathy and blood samples from normal subjects to identify differential expression of lncRNAs and mRNAs. The lncRNA-miRNA-mRNA network was constructed using bioinformatics tools, and functional and pathway enrichment analysis and protein-protein interactions were performed. The mRNAs in the network and the proteins they encode are then used as targets for predicting drugs. Besides, the expression of lncRNAs in the ceRNA network was validated by real-time quantitative PCR (qRT-PCR) experiments in vitro. Results. The differentially expressed lncRNA-miRNA-mRNA ceRNA network in dilated cardiomyopathy was successfully established. Two differentially overexpressed key lncRNAs were found from the network: AC093817 and AC091062, and qRT-PCR experiments further validated the overexpression of AC093817 and AC091062. The mRNAs in the network and the proteins encoded by the mRNAs were used for drug prediction to get related drugs. Conclusion. This study supports a possible mechanism and drug development of dilated cardiomyopathy, AC093817 and AC091062 being potential biomarkers of dilated cardiomyopathy

Ultra‐high performance liquid chromatography coupled to tandem mass spectrometry‐based metabolomics study of diabetic distal symmetric polyneuropathy

Abstract Aims/Introduction Distal symmetric polyneuropathy (DSPN) is a common complication of type 2 diabetes mellitus, but the underlining mechanisms have not yet been elucidated. The current study was designed to screen the feature metabolites classified as potential biomarkers, and to provide deeper insights into the underlying distinctive metabolic changes during disease progression. Materials and Methods Plasma metabolite profiles were obtained by the ultra‐high liquid chromatography coupled to tandem mass spectrometry method from healthy control participants, patients with type 2 diabetes mellitus and patients with DSPN. Potential biomarkers were selected through comprehensive analysis of statistically significant differences between groups. Results Overall, 938 metabolites were identified. Among them, 12 metabolites (dimethylarginine, N6‐acetyllysine, N‐acetylhistidine, N,N,N‐trimethyl‐alanylproline betaine, cysteine, 7‐methylguanine, N6‐carbamoylthreonyladenosine, pseudouridine, 5‐methylthioadenosine, N2,N2‐dimethylguanosine, aconitate and C‐glycosyl tryptophan) were identified as the specific biomarkers. The content of 12 metabolites were significantly higher in the DSPN group compared with the other two groups. Additionally, they showed good performance to discriminate the DSPN state. Correlation analyses showed that the levels of 12 metabolites might be more closely related to the glucose metabolic changes, followed by the levels of lipid metabolism. Conclusions The finding of the 12 signature metabolites might provide a novel perspective for the pathogenesis of DSPN. Future studies are required to test this observation further

MF-SRCDNet: Multi-feature fusion super-resolution building change detection framework for multi-sensor high-resolution remote sensing imagery

Building change detection is essential for evaluating land use, land cover change, and sustainable development. However, owing to the mismatched resolutions from multi-sensors and the complexity of the features of high-resolution images, traditional methods of building change detection have problems with accuracy and applicability. In this study, we propose a deep-learning-based multi-feature fusion super-resolution building change detection framework (MF-SRCDNet), comprising super-resolution (SR), multi-feature fusion, and change detection (CD) modules. The SR module introduces a Res-UNet network to generate unified SR images with rich semantic information. To enhance the performance of MF-SRCDNet for complex building detection, an effective right-angle edge vision feature was designed and fused with a CD module with an improved feature extractor. The proposed method achieved the highest F1 values of 0.881, 0.857, and 0.964 for the three datasets, respectively, compared with different modules. The results also show improved robustness in different bi-temporal image resolution scale-difference experiments. The method proposed in this study can be applied to a variety of complex scenarios for building CD tasks with strong model generalization

Expression of hepatic miRNAs targeting porcine glucocorticoid receptor (GR) 3′UTR in the neonatal piglets under a maternal gestational betaine supplementation

Glucocorticoid receptor (GR) has been previously demonstrated an important transcriptional factor of hepatic metabolic genes in the neonates under a maternal gestational betaine supplementation (“Gestational dietary betaine supplementation suppresses hepatic expression of lipogenic genes in neonatal piglets through epigenetic and glucocorticoid receptor-dependent mechanisms” Cai et al., 2015 [1]). Here we provide accompanying data about the expression of hepatic miRNAs targeting porcine GR 3′UTR in the neonatal piglets. Liver samples were obtained and RNA was isolated. RNA was polyadenylated by poly (A) polymerase and then dissolved and reverse transcribed using poly (T) adapter. The diluted cDNA were used in each real-time PCR assay. The sequences of all the porcine miRNAs were acquired from miRBase (http://www.mirbase.org/). miRNAs targeting GR were predicted using the PITA algorithm. Among all the predicted miRNAs, 4 miRNAs targeting GR were quantitated by real-time PCR and miRNA-124a, which has been identified to target GR 3′UTR [2,3], was more highly expressed in betaine-exposed neonatal livers. Keywords: miRNAs, GR, Betaine, Neonatal live

Immune-Related Genes: Potential Regulators and Drug Therapeutic Targets in Hypertrophic Cardiomyopathy

Background. Accumulating evidence shows that the innate immune system is a key player in cardiovascular repair and regeneration, but little is known about the role of immune-related genes (IRGs) in hypertrophic cardiomyopathy (HCM). Methods. The differential mRNA expression profiles of HCM samples were downloaded from the Gene Expression Omnibus (GEO) dataset (GSE89714), and the IRG expression profile was obtained from the ImmPort database. The regulatory pathways of IRGs in HCM were screened out through discrepantly expressive genes (DEGs) analysis, enrichment of gene function/pathway analysis, and protein-protein interaction (PPI) network. Besides, hub IRGs in the PPI network were selected for drug prediction. Results. A total of 854 genes were differentially expressed in HCM, of which 88 were IRGs. Functional enrichment analysis revealed that 88 IRGs were mainly involved in the biological processes (BP) of SMAD protein pathway, smooth muscle cell proliferation, protein serine/threonine kinase, and mitogen-activated protein kinase (MAPK) cascade. Cytokine-cytokine receptor interaction, TGFβ signaling pathway, PI3K-Akt signaling pathway, and MAPK signaling pathway were enriched in the pathway enrichment analysis of these 88 IRGs. Furthermore, the PPI regulatory network of IRGs was constructed, and 10 hub IRGs were screened out to construct a regulatory network for HCM. 4 transcription factors (TFs) were the major regulator of 10 hub IRGs. Finally, these 10 hub IRGs were entered into the pharmacogenomics database for prediction, and the relevant drugs were obtained. Conclusions. In this study, 10 hub IRGs were coexpressed with 4 TFs to construct a regulatory network for HCM. Drug prediction of these 10 hub IRGs proposed potential therapeutic agents that could be used in HCM. These results indicate that IRGs are potential regulators and drug therapeutic targets in HCM

Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic

Monitoring the fine spatiotemporal distribution of urban GDP is a critical research topic for assessing the impact of the COVID-19 outbreak on economic and social growth. Based on nighttime light (NTL) images and urban land use data, this study constructs a GDP machine learning and linear estimation model. Based on the linear model with better effect, the monthly GDP of 34 cities in China is estimated and the GDP spatialization is realized, and finally the GDP spatiotemporal correction is processed. This study analyzes the fine spatiotemporal distribution of GDP, reveals the spatiotemporal change trend of GDP in China’s major cities during the current COVID-19 pandemic, and explores the differences in the economic impact of the COVID-19 pandemic on China’s major cities. The result shows: (1) There is a significant linear association between the total value of NTL and the GDP of subindustries, with R2 models generated by the total value of NTL and the GDP of secondary and tertiary industries being 0.83 and 0.93. (2) The impact of the COVID-19 pandemic on the GDP of cities with varied degrees of development and industrial structures obviously varies across time and space. The GDP of economically developed cities such as Beijing and Shanghai are more affected by COVID-19, while the GDP of less developed cities such as Xining and Lanzhou are less affected by COVID-19. The GDP of China’s major cities fell significantly in February. As the COVID-19 outbreak was gradually brought under control in March, different cities achieved different levels of GDP recovery. This study establishes a fine spatial and temporal distribution estimation model of urban GDP by industry; it accurately monitors and assesses the spatial and temporal distribution characteristics of urban GDP during the COVID-19 pandemic, reveals the impact mechanism of the COVID-19 pandemic on the economic development of major Chinese cities. Moreover, economically developed cities should pay more attention to the spread of the COVID-19 pandemic. It should do well in pandemic prevention and control in airports and stations with large traffic flow. At the same time, after the COVID-19 pandemic is brought under control, they should speed up the resumption of work and production to achieve economic recovery. This study provides scientific references for COVID-19 pandemic prevention and control measures, as well as for the formulation of urban economic development policies

Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning

Puerarin suppresses the hepatic gluconeogenesis via activation of PI3K/Akt signaling pathway in diabetic rats and HepG2 cells

Pueraria, a Chinese herbal medicine, plays an important role in many classic prescriptions for the treatment of diabetes. Puerarin is the main component of pueraria. The current in vivo and in vitro research mainly focus on exploring the potential mechanism of puerarin in inhibiting hepatic gluconeogenesis. The type 2 diabetic rats were established by a combination of small dosage of streptozotocin (STZ) injection with high-fat diet. After the administration of puerarin 4 weeks, the parameters of the glucose and lipid metabolism were determined. HepG2 cells were treated by palmitic acid (PA) to induce the insulin resistance in vitro model. After the treatment of puerarin, the glucose consumption and cell viability were examined. Then, the protein expression of PI3K, Akt, pAkt, pFOXO1, FOXO1, PEPCK and G6pase in liver tissue and HepG2 cells were evaluated by western blot. RT-PCR was used to measure the content of PEPCK, G6pase mRNA in liver tissue. The results showed that puerarin administration significantly decrease the level of FBG, HbA1C and triglycerides in diabetic rats. Mechanistic research showed that puerarin activating PI3K/Akt is puerarin-mediated beneficial effects and can be reversed by inhibitor of PI3K or Akt. In conclusion, puerarin inhibits hepatic gluconeogenesis by activating PI3K/Akt signaling pathway