Effect of cucurbitacin on malignant biological behavior of breast cancer cells, and its possible underlying mechanism

Purpose: To study the influence of cucurbitacin on malignant biological behavior of mammary carcinoma cells, and the likely mechanism involved.Methods: Human mammary carcinoma cell line MDA-MB-436 was selected for cell culture and treated with different concentrations of cucurbitacin. The effect of cucurbitacin on cell activity, cell colonyformation capacity, cell invasion, migration potential, matrix metalloproteinase-9 (MMP-9) activity, and levels of vascular endothelial growth factor A (VEGFA), epithelial calcium adhesion (E-cadherin), and neurogenic calcium adhesion (N-cadherin) were measured. Moreover, levels of wave protein (vimentin), phosphorylated epidermal growth factor receptor (p-EGFR), phosphorylated signaling transduction, and transcription activation factor 3 (p-STAT3) and phosphorylated protein kinase B (p-Akt) were determined.Results: With increase in cucurbitacin dose, there was significant decrease in cell viability, cell colony ratio, cell invasion and migration capacity, and expression levels of MMP-9, VEGFA, e-cadherin, ncadherin, vimentin, P-EGFR, P-STAT3 and p-Akt (p < 0.05).Conclusion: Cucurbitacin inhibits the proliferation, invasion, and migration of breast cancer cells by down-regulating the expressions of EGFR/STAT3/Akt signaling-related proteins, and inhibiting epithelial-mesenchymal transition transformation

The roles of serum vitamin D and tobacco smoke exposure in insomnia: a cross-sectional study of adults in the United States

AimTobacco smoke exposure and vitamin D (VD) status were both associated with insomnia. However, the combined effect of smoking and VD on insomnia has not been discussed. This study aimed to explore the role of VD in the association between tobacco smoke exposure and insomnia.MethodsData on adults were extracted from the National Health and Nutrition Examination Surveys (NHANES) database in 2005–2008 for this cross-sectional study. Weighted univariate and multivariate logistic regression analyses were used to explore the associations between serum cotinine, serum VD, and insomnia. A surface diagram was drawn to reflect the effect of VD on the association between serum cotinine and insomnia. In addition, the potential regulating effect of VD in subgroups of smoking status was also performed. The evaluation index was odds ratios (ORs) with 95% confidence intervals (CIs).ResultsAmong the eligible participants, 1,766 had insomnia. After adjusting for covariates, we found that elevated serum cotinine levels were associated with higher odds of insomnia [OR = 1.55, 95% CI: (1.22, 1.97)]. However, the relationship between serum VD level and insomnia was not significant (P = 0.553). Higher serum cotinine levels were also associated with higher odds of insomnia [OR = 1.52, 95% CI: (1.17, 1.98)] when serum VD level was <75 nmol/L; however, this relationship became non-significant when serum VD concentration was elevated (P = 0.088). Additionally, the potential regulating effect of VD was also found in adults who were not smoking.ConclusionVD may play a potential regulative role in the association between tobacco smoke exposure and insomnia. Further studies are needed to clarify the causal relationships between VD, tobacco smoke exposure, and insomnia

Experimental verification and identifying biomarkers related to insomnia

IntroductionInsomnia is the most common form of sleep deprivation (SD) observed in clinics. Although there are differences between insomnia and SD, they have similar symptoms and the same animal model. Currently, there is a lack of microarray data on insomnia. Therefore, for now, we are going to apply the SD data to insomnia. Although many studies have explained the possible mechanisms associated with insomnia, no previous studies have considered the key genes associated with insomnia or the relationship between insomnia and immune cells. In this study, we analyzed the relationship between key genes and immune cells by identifying biomarkers for the diagnosis of insomnia. Next, we verified the efficacy of these biomarkers experimentally.MethodsFirst, we downloaded four microarrays (GSE11755, GSE12624, GSE28750, and GSE48080) from the Gene Expression Omnibus (GEO) database, which included data from 239 normal human blood samples and 365 blood specimens from patients with SD. Then, we analyzed two groups of differentially expressed genes (DEGs) and used Support Vector Machine Recursive Feature Elimination (SVM-RFE) analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to investigate these key genes. Next, we used CIBERSORT to investigate the composition of 22 immune cell components of key genes in SD patients. Finally, the expression levels of key biomarkers in sleep-deprived patients were examined by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 50 DEGs were identified: six genes were significantly upregulated, and 44 genes were significantly downregulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that Salmonella infection, NOD-like receptor (NLR) signaling pathway, Kaposi sarcoma-associated herpesvirus infection, and Th17 cell differentiation were significant. Based on machine learning, we identified C2CD2L, SPINT2, APOL3, PKNOX1, and A2M as key genes for SD; these were confirmed by receiver operating characteristic (ROC) analysis. Immune cell infiltration analysis showed that C2CD2L, SPINT2, APOL3, PKNOX1, and A2M were related in different degrees to regulatory T cells (Tregs), follicular T helper cells, CD8 cells, and other immune cells. The qRT-PCR experiments confirmed that the expression levels of C2CD2L concurred with the results derived from machine learning, but PKNOX1 and APOL3 did not.DiscussionIn summary, we identified a key gene (C2CD2L) that may facilitate the development of biomarkers for insomnia

Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification

Sepsis is a systemic inflammatory response syndrome caused by bacteria and other pathogenic microorganisms. Every year, approximately 31.5 million patients are diagnosed with sepsis, and approximately 5.3 million patients succumb to the disease. In this study, we identified biomarkers for diagnosing sepsis analyzed the relationships between genes and Immune cells that were differentially expressed in specimens from patients with sepsis compared to normal controls. Finally, We verified its effectiveness through animal experiments. Specifically, we analyzed datasets from four microarrays(GSE11755、GSE12624、GSE28750、GSE48080) that included 106 blood specimens from patients with sepsis and 69 normal human blood samples. SVM-RFE analysis and LASSO regression model were carried out to screen possible markers. The composition of 22 immune cell components in patients with sepsis were also determined using CIBERSORT. The expression level of the biomarkers in Sepsis was examined by the use of qRT-PCR and Western Blot (WB). We identified 50 differentially expressed genes between the cohorts, including 2 significantly upregulated and 48 significantly downregulated genes, and KEGG pathway analysis identified Salmonella infection, human T cell leukemia virus 1 infection, Epstein−Barr virus infection, hepatitis B, lysosome and other pathways that were significantly enriched in blood from patients with sepsis. Ultimately, we identified COMMD9, CSF3R, and NUB1 as genes that could potentially be used as biomarkers to predict sepsis, which we confirmed by ROC analysis. Further, we identified a correlation between the expression of these three genes and immune infiltrate composition. Immune cell infiltration analysis revealed that COMMD9 was correlated with T cells regulatory (Tregs), T cells follicular helper, T cells CD8, et al. CSF3R was correlated with T cells regulatory (Tregs), T cells follicular helper, T cells CD8, et al. NUB1 was correlated with T cells regulatory (Tregs), T cells gamma delta, T cells follicular helper, et al. Taken together, our findings identify potential new diagnostic markers for sepsis that shed light on novel mechanisms of disease pathogenesis and, therefore, may offer opportunities for therapeutic intervention

Characterization of genome-wide H3K27ac profiles reveals a distinct PM2.5-associated histone modification signature

Complete list of differentially modified H3K27ac loci. (XLSX 69 kb

Consequences of False-Positive Screening Mammograms

False-positive mammograms, a common occurrence in breast cancer screening programs, represent a potential screening harm that is currently being evaluated by the United States Preventive Services Task Force

Coupling dynamics of a geared multibody system supported by Elastohydrodynamic lubricated cylindrical joints

A comprehensive computational methodology to study the coupling dynamics of a geared multibody system supported by ElastoHydroDynamic (EHD) lubricated cylindrical joints is proposed throughout this work. The geared multibody system is described by using the Absolute-Coordinate-Based (ACB) method that combines the Natural Coordinate Formulation (NCF) describing rigid bodies and the Absolute Nodal Coordinate Formulation (ANCF) characterizing the flexible bodies. Based on the finite-short bearing approach, the EHD lubrication condition for the cylindrical joints supporting the geared system is considered here. The lubrication forces developed at the cylindrical joints are obtained by solving the Reynolds’ equation via the finite difference method. For the evaluation of the normal contact forces of gear pair along the Line Of Action (LOA), the time-varying mesh stiffness, mesh damping and Static Transmission Error (STE) are utilized. The time-varying mesh stiffness is calculated by using the Chaari’s methodology. The forces of sliding friction along the Off-Line-Of-Action (OLOA) are computed by using the Coulomb friction models with a time-varying coefficient of friction under the EHD lubrication condition of gear teeth. Finally, two numerical examples of application are presented to demonstrate and validate the proposed methodology.National Natural Science Foundations of China under Grant 11290151, 11221202 and 11002022, Beijing Higher Education Young Elite Teacher Project under Grant YETP1201

Real-Time Multi-Target Localization from Unmanned Aerial Vehicles

In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1) the real-time zoom lens distortion correction method; (2) a recursive least squares (RLS) filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP) of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions