Objective To investigate the clinical effect of ulinastatin in preventing pancreatitis after endoscopic retrograde cholangiopancreatography( ERCP). Methods The Cochrane Library,Pub ...Med,EMBASE,CNKI,VIP,and Wanfang Data were searched for randomized controlled trials( RCTs) on ulinastatin for the prevention of post- ERCP pancreatitis published from 1970 to June 2016. Two researchers selected RCTs,extracted data,and evaluated methodological quality independently,and Rev Man 5. 3 software was used for the meta- analysis. The chi- square test was used for the heterogeneity analysis of RCTs included,and the funnel plots were used to evaluate publication bias. Results A total of six RCTs with 923 patients were included in this analysis. Compared with the placebo,ulinastatin had significantly better effects in preventing post- ERCP pancreatitis( OR = 0. 26,95% CI: 0. 13- 0. 53,P = 0. 000 2),hyperamylasemia( OR = 0. 47,95% CI: 0. 33- 0. 67,P 0. 001),and abdominal pain( OR = 0. 56,95% CI: 0. 34- 0. 91,P = 0. 020). Compared with gabexate,ulinastatin had similar effects in preventing post- ERCP pancreatitis,hyperamylasemia,and abdominal pain( P = 0. 52,0. 13,and 0. 79); low- dose ulinastatin also had similar effects as gabexate in preventing post- ERCP pancreatitis and hyperamylasemia( P = 0. 49 and0. 25). The funnel plots based on the effect of ulinastatin in preventing post- ERCP pancreatitis were slightly asymmetric,which suggested the presence of publication bias. Conclusion Ulinastatin( > or =15 x 104U) can effectively prevent post- ERCP pancreatitis,hyperlipidemia,and abdominal pain in the general population and it is recommended to start using this drug before surgery.
Nonalcoholic fatty liver disease (NAFLD) is prevalent worldwide; about 25% of NAFLD silently progress into steatohepatitis, in which some of them may develop into fibrosis, cirrhosis and liver ...failure. However, few drugs are available for NAFLD, partly because of an incomplete understanding of its pathogenic mechanisms. Here, using in vivo and in vitro gain- and loss-of-function approaches, we identified up-regulated DKK1 plays a pivotal role in high-fat diet-induced NAFLD and its progression. Mechanistic analysis reveals that DKK1 enhances the capacity of hepatocytes to uptake fatty acids through the ERK-PPARγ-CD36 axis. Moreover, DKK1 increased insulin resistance by activating the JNK signaling, which in turn exacerbates disorders of hepatic lipid metabolism. Our finding suggests that DKK1 may be a potential therapeutic and diagnosis candidate for NAFLD and metabolic disorder progression.
The dysregulation of exosomal microRNAs (miRNAs) plays a crucial role in the development and progression of cancer. This study investigated the role of a newly identified serum exosomal miRNA ...miR-4256 in gastric cancer (GC) and the underlying mechanisms. The differentially expressed miRNAs were firstly identified in serum exosomes of GC patients and healthy individuals using next-generation sequencing and bioinformatics. Next, the expression of serum exosomal miR-4256 was analyzed in GC cells and GC tissues, and the role of miR-4256 in GC was investigated by
and
experiments. Then, the effect of miR-4256 on its downstream target genes HDAC5/p16
was studied in GC cells, and the underlying mechanisms were evaluated using dual luciferase reporter assay and Chromatin Immunoprecipitation (ChIP). Additionally, the role of the miR-4256/HDAC5/p16
axis in GC was studied using
and
experiments. Finally, the upstream regulators SMAD2/p300 that regulate miR-4256 expression and their role in GC were explored using
experiments. miR-4256 was the most significantly upregulated miRNA and was overexpressed in GC cell lines and GC tissues;
and
results showed that miR-4256 promoted GC growth and progression. Mechanistically, miR-4256 enhanced HDAC5 expression by targeting the promoter of the HDAC5 gene in GC cells, and then restrained the expression of p16
through the epigenetic modulation of HDAC5 at the p16
promoter. Furthermore, miR-4256 overexpression was positively regulated by the SMAD2/p300 complex in GC cells. Our data indicate that miR-4256 functions as an oncogene in GC via the SMAD2/miR-4256/HDAC5/p16
axis, which participates in GC progression and provides novel therapeutic and prognostic biomarkers for GC.
Atomic force microscope (AFM) is a powerful and prospective tool in the studies of the cellular deformability within a single cell. However, the reliability of using AFM in estimating the cellular ...average deformability was suspected. Hemorheology, one of common clinical examinations, can assess the deformability of erythrocytes. In order to validate the reliability in estimating the cellular deformability with AFM, the correlation was studied between the stiffness of the erythrocyte membrane with AFM and the deformation index with the hemorheology test. Blood samples were taken from 5 diabetes mellitus patients and 5 healthy non-obese people. The erythrocyte deformability was detected by AFM and the hemorheology test, respectively. Excellent correlation (r=0.907, p=0.000<0.01) was found between the average erythrocyte stiffness with AFM and the deformation index obtained from the hemorheology test. Besides, it was found that the average stiffness and the deformation index of diabetes mellitus patients were significantly higher than those of healthy non-obese people (p=0.007 and p=0.003). The result was consistent with the impairment of erythrocytes form diabetes mellitus patients. Therefore, it is the first time that the quantification relation of the erythrocyte deformability was investigated with AFM and the hemorheology test, and the feasible and reliable were validated that AFM is used to investigate the mechanical properties of different living cells qualitatively and quantitatively.
On the basis of a simple snow-atmosphere-soil transfer （SAST） model previously developed, this paper presents an improved snow-atmosphere-soil transfer （ISAST） model that has a new numerical scheme ...and an improved method of layering the snowpack. The new model takes the snow cover temperature and ice content in the snow cover as prognostic variables. This approach, which effectively solves the snow cover temperature distribution when the snow cover is melting or freezing, lessens the iteration time and computation time, which is important for GCM simulation. In this model, the snow cover is divided into three layers （ISAST3） or seven layers （ISAST7）. The simulation results obtained using the ISAST7 model agree well with ob- servations in terms of snow depth, snow equivalent water and snow cover lifetime at five Russian sites. The new ISAST model has better simulation capacity for snow cover than the previous SAST model. When the snow cover is deep, the simulation of the ISAST7 model is better than that of the ISAST3 model. Testing shows that our ISAST model is approximately 20% faster than the SAST model.
Global vegetation dynamics are of critical importance for understanding changes in ecosystem structure and functioning and their responses to different natural and anthropogenic drivers. Under the ...background of rapid global warming, it is still unclear whether there were significant changes in the extent and intensity of global vegetation browning during the past three decades. Taking satellite-derived normalized difference vegetation index (NDVI) as the proxy of vegetation growth, we investigated spatiotemporal variances in global vegetation trends during the period 1982–2013 using the ensemble empirical mode decomposition (EEMD) method and two piecewise linear regression models. Our study suggests that increasing global vegetation browning is masked by overall vegetation greening. A >60% increase in browning area was found during the study period, and the results consistently indicate that the expansion of browning trends has accelerated since 1994. After the late 1990s, browning trends increased in all latitudinal bands in the Northern Hemisphere. This increase was particularly pronounced in the northern mid-low latitudes, where the greening trends stalled or even reversed. Areas with browning trends increased in all land cover types, although the increase processes varied substantially. During 1982–2013, although most vegetated lands exhibited overall greening trends, greening-to-browning reversals occurred on all continents and occupied a much larger area than browning-to-greening reversals. Greening trends prevailed before the turning points, and browning trends largely expanded and enhanced thereafter. The increased browning trends resulted in a slowdown of the increase in global mean NDVI since the early 1990s. Since drought is likely the main cause of the increasing browning trends, global vegetation growth is at risk of reversal from long-term greening to long-term browning in the warmer future.
•This study revealed the spatiotemporal changes in global NDVI during 1982–2013.•This study synthesized the results of EEMD and two piecewise regression models.•Global browning trends were largely expanded and enhanced since the early 1990s.•Greening-to-browning reversals were widespread and occurred on all continents.
With recent advances in single-cell RNA sequencing, enormous transcriptome datasets have been generated. These datasets have furthered our understanding of cellular heterogeneity and its underlying ...mechanisms in homogeneous populations. Single-cell RNA sequencing (scRNA-seq) data clustering can group cells belonging to the same cell type based on patterns embedded in gene expression. However, scRNA-seq data are high-dimensional, noisy, and sparse, owing to the limitation of existing scRNA-seq technologies. Traditional clustering methods are not effective and efficient for high-dimensional and sparse matrix computations. Therefore, several dimension reduction methods have been introduced. To validate a reliable and standard research routine, we conducted a comprehensive review and evaluation of four classical dimension reduction methods and five clustering models. Four experiments were progressively performed on two large scRNA-seq datasets using 20 models. Results showed that the feature selection method contributed positively to high-dimensional and sparse scRNA-seq data. Moreover, feature-extraction methods were able to promote clustering performance, although this was not eternally immutable. Independent component analysis (ICA) performed well in those small compressed feature spaces, whereas principal component analysis was steadier than all the other feature-extraction methods. In addition, ICA was not ideal for fuzzy C-means clustering in scRNA-seq data analysis. K-means clustering was combined with feature-extraction methods to achieve good results.
Increasing studies show that circular RNAs (circRNAs) play vital roles in tumour progression. But, how circRNAs function in ovarian cancer is mostly unclear. Here, we detected the expression of ...circEPSTI1 in ovarian cancer and explored the function of circEPSTI1 in ovarian cancer via a series of experiments. Then, we performed luciferase assay and RNA immunoprecipitation (RIP) assay to explore the competing endogenous RNA (ceRNA) function of circEPSTI1 in ovarian cancer. qRT‐PCR verified that circEPSTI1 was overexpressed in ovarian cancer. Inhibition of circEPSTI1 suppressed ovarian cancer cell proliferation, invasion but promoted cell apoptosis. Luciferase assays and RIP assay showed that circEPSTI1 and EPSTI1 (epithelial stromal interaction 1) could directly bind to miR‐942. And circEPSTI1 could regulate EPSTI1 expression via sponging miR‐942. In summary, circEPSTI1 regulated EPSTI1 expression and ovarian cancer progression by sponging miR‐942. circEPSTI1 could be used as a biomarker and therapeutic target in ovarian cancer.
Medical image segmentation is a fundamental step in medical analysis and diagnosis. In recent years, deep learning networks have been used for precise segmentation. Numerous improved encoder-decoder ...structures have been proposed for various segmentation tasks. However, high-level features have gained more research attention than the abundant low-level features in the early stages of segmentation. Consequently, the learning of edge feature maps has been limited, which can lead to ambiguous boundaries of the predicted results. Inspired by the encoder-decoder network and attention mechanism, this study investigates a novel multilayer edge attention network (MEA-Net) to fully utilize the edge information in the encoding stages. MEA-Net comprises three major components: a feature encoder module, a feature decoder module, and an edge module. An edge feature extraction module in the edge module is designed to produce edge feature maps by a sequence of convolution operations so as to integrate the inconsistent edge information from different encoding stages. A multilayer attention guidance module is designed to use each attention feature map to filter edge information and select important and useful features. Through experiments, MEA-Net is evaluated on four medical image datasets, including tongue images, retinal vessel images, lung images, and clinical images. The evaluation values of the Accuracy of four medical image datasets are 0.9957, 0.9736, 0.9942, and 0.9993, respectively. The values of the Dice coefficient are 0.9902, 0.8377, 0.9885, and 0.9704, respectively. Experimental results demonstrate that the network being studied outperforms current state-of-the-art methods in terms of the five commonly used evaluation metrics. The proposed MEA-Net can be used for the early diagnosis of relevant diseases. In addition, clinicians can obtain more accurate clinical information from segmented medical images.
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