The role of platelet-derived extracellular vesicles (PEVs) in the development of sepsis was investigated in this study.After collection of blood samples from sepsis patients and normal volunteers, the extracellular vesicles (EVs) were separated, followed by the isolation of PEVs from the blood of rats. Next, a sepsis rat model was constructed by cecal ligation and puncture (CLP), and rats received tail vein injection of PEVs to explore the role of PEVs in sepsis. Subsequently, nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM) were adopted to determine the diameter of EVs and observe the morphology of PEVs, respectively; flow cytometry to detect the percentage of CD41-and CD61-positive EVs in isolated EVs; and ELISA to assess neutrophil extracellular trap (NET) formation, endothelial function injury-related markers in clinical samples or rat blood and serum inflammatory factor level.Compared with normal volunteers, the percentage of CD41- and CD61-positive EVs and the number of EVs were significantly elevated in sepsis patients. Moreover, sepsis patients also presented notably increased histone H3, myeloperoxidase (MPO), angiopoietin-2 and endocan levels in the blood, and such increase was positively correlated with the number of EVs. Also, animal experiments demonstrated that PEVs significantly promoted NET formation, mainly manifested as up-regulation of histone H3, high mobility group protein B1 (HMGB1), and MPO; promoted endothelial dysfunction (up-regulation of angiopoietin-2, endocan, and syndecan-1); and stimulated inflammatory response (up-regulation of interleukin (IL) -1β, IL-6, tumor necrosis factor (TNF)-α, and monocyte chemoattractant protein (MCP) -1) in the blood of sepsis rats.PEVs aggravate endothelial function injury and inflammatory response in sepsis by promoting NET formation.
The purpose of the present study is to screen the hub genes associated with sepsis, comprehensively understand the occurrence and progress mechanism of sepsis, and provide new targets for clinical diagnosis and treatment of sepsis.The microarray data of GSE9692 and GSE95233 were downloaded from the Gene Expression Omnibus (GEO) database. The dataset GSE9692 contained 29 children with sepsis and 16 healthy children, while the dataset GSE95233 included 102 septic subjects and 22 healthy volunteers. Differentially expressed genes (DEGs) were screened by GEO2R online analysis. The DAVID database was applied to conduct functional enrichment analysis of the DEGs. The STRING database was adopted to acquire protein-protein interaction (PPI) networks.We identified 286 DEGs (217 upregulated DEGs and 69 downregulated DEGs) in the dataset GSE9692 and 357 DEGs (236 upregulated DEGs and 121 downregulated DEGs) in the dataset GSE95233. After the intersection of DEGs of the two datasets, a total of 98 co-DEGs were obtained. DEGs associated with sepsis were involved in inflammatory responses such as T cell activation, leukocyte cell-cell adhesion, leukocyte-mediated immunity, cytokine production, immune effector process, lymphocyte-mediated immunity, defense response to fungus, and lymphocyte-mediated immunity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that sepsis was connected to bacterial and viral infections. Through PPI network analysis, we screened the most important hub genes, including ITK, CD247, MMP9, CD3D, MMP8, KLRK1, and GZMK.In conclusion, the present study revealed an unbalanced immune response at the transcriptome level of sepsis and identified genes for potential biomarkers of sepsis, such as ITK, CD247, MMP9, CD3D, MMP8, KLRK1, and GZMK.
Based on ten cities’ workers data about the employment situation of the worker in 2010, the paper analyzes the influence of labor contract and labor union on social security rights by using binary logistic regression model. The study found that: 1) labor contract and labor union contribute to the acquisition of the workers’ social security right, and the promotion of labor contract is greater than that of labor union; 2) the impact of labor contract and labor union on different types of social insurance is also different, which is more reflected in the unemployment insurance and basic medical insurance, while less reflected in the work injury insurance.
The objective of this study was to explore rehabilitation of patients with acute kidney injury (AKI) treated with Xuebijing injection by using intelligent medical big data analysis system. Based on Hadoop distributed processing technology, this study designed a medical big data analysis system and tested its performance. Then, this analysis system was used to systematically analyze rehabilitation of sepsis patients with AKI treated with Xuebijing injection. It is found that the computing time of this system does not increase obviously with the increase of cases. The results of systematic analysis showed that the glomerular filtration rate (59.31 ± 3.87% vs 44.53 ± 3.53%) in the experimental group was obviously superior than that in the controls after one week of treatment. The levels of urea nitrogen (9.32 ± 2.21 mmol/L vs. 14.32 ± 0.98 mmol/L), cystatin C (1.65 ± 0.22 mg/L vs. 2.02 ± 0.13 mg/L), renal function recovery time (6.12 ± 1.66 days vs. 8.66 ± 1.17 days), acute physiology and chronic health evaluation system score (8.98 ± 2.12 points vs. 12.45 ± 2.56 points), sequential organ failure score (7.22 ± 0.86 points vs. 8.61 ± 0.97 points), traditional Chinese medicine (TCM) syndrome score (6.89 ± 1.11 points vs. 11.33 ± 1.23 points), and ICU time (16.43 ± 2.37 days vs. 12.15 ± 2.56 days) in the experimental group were obviously lower than those in the controls, and the distinctions had statistical significance (P < 0.05). The significant efficiency (37.19% vs. 25.31%) and total effective rate (89.06% vs. 79.06%) in the experimental group were obviously superior than those in the controls, and distinction had statistical significance (P < 0.05). In summary, the medical big data analysis system constructed in this study has high efficiency. Xuebijing injection can improve the renal function of sepsis patients with kidney injury, and its therapeutic effect is obviously better than that of Western medicine, and it has clinical application and promotion value.
Sepsis is a global public health burden. The sequential organ failure assessment (SOFA) is the most commonly used scoring system for diagnosing sepsis and assessing severity. Due to the widespread use of endotracheal intubation and sedative medications in sepsis, the accuracy of the Glasgow Coma Score (GCS) is the lowest in SOFA. We designed this multicenter, cross-sectional study to investigate the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis.
Based on the result evaluation of measures used to enhance oil production of fractured wells,a sample database with different types of measures and processes is established.Main factors taken into account in the database include the effective perforation thicknesses of whole wells,the formation coefficients of fractured layers,the pre-fracturing liquid production,the pre-fracturing water cut,the number of fracturing layers,and the total sand volume.Quantitative relationship is established between fracturing results and those factors mentioned above by employing the artificial neural network method.A model used to predict fracturing results is therefore established.Field application shows that the prediction results on the basis of the method are more reliable.
Abstract Background Currently, there are no effective tools to accurately assess acute biliary pancreatitis (ABP) risk in patients with gallstones. This study aimed to develop an ABP risk nomogram in patients with gallstones. Methods Data on 2102 patients with gallstones admitted to the Department of General Surgery of The First Affiliated Hospital of Harbin Medical University between January 6, 2009 and January 22, 2018 were retrospectively collected. Some patients were randomly divided into the training cohort (n=1470) for nomogram development; the others formed the validation cohort (n=632) to confirm the model’s performance. The chi-square test was used to optimize feature selection, and logistic regression analysis was applied to build a prediction model incorporating the selected features. The area under the curve (AUC), Hosmer-Lemeshow test, calibration curve, decision curve analysis (DCA) and internal validation were used to validate the model’s accuracy, calibration and clinical effectiveness. Results Predictors included sex (male), diabetes, gallbladder wall thickness ≤3 mm, gallbladder stone diameter <3 mm, coexisting CBD stone, CBD diameter ≤10 mm, AST≥53.6 U/L, GGT≥150 U/L, DBIL≥1.0 mg/dL, WBC≥10Í10 9 , and GRAN%≥80%. The model displayed good predictive power with AUCs of 0.850 (95% CI: 0.825~0.875) and 0.844 (95% CI: 0.825~0.875) in the training and validation cohorts, respectively. The DCA showed a 10-100% risk threshold. The Hosmer-Lemeshow test and calibration curve demonstrated the accuracy and effectiveness of this model, which could be applied in clinical practice. Conclusions The ABP risk nomogram incorporating 11 features is useful to predict ABP risk in gallstone patients.
A new Co(II)-containing coordination polymer formulated as [Co(H-ntb)]n (1) has been synthesized via reaction of Co(NO3)2·6H2O with the 4,4′,4′'-nitrilotribenzoate (H3ntb) ligand under solvothermal conditions. In addition, the enhancement of the compound on Fentanyl analgesic activity was evaluated. First, the RT-PCR was carried out to assess the glutamate receptor relative expression level on sympathetic nerve cells. Then, the mouse hot plate analgesic experiment was performed for the evaluation of the enhancement of the compound on Fentanyl analgesic activity. In the end, molecular docking simulation has been performed to investigate the possible interactions between the probe protein and the Co(II) complex.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)