BackgroundThe distribution of adipose tissue has been evaluated in relation to cardiovascular risk factors and biochemical components of the metabolic syndrome. Neck circumference (NC) has been shown to have a strong relationship with cardiovascular disease (CVD) and may be a novel indicator of CVD. The aim of this study was to compare the incidence of CVD events in cohorts with different NC distributions, and to correlate NC with future CVD events and relative mortality.MethodsA prospective cohort study was performed on 12,151 high-risk cardiology outpatients from 2004 until 2014. Anthropometric parameters like body mass index, NC, waist circumference, and hip circumference were measured at baseline and follow-up and compared in different cohorts with high, medium, and low NC. Fatal and non-fatal CVD events were compared in the follow-up study, and survival analysis was conducted. Independent Chi-square tests were performed to compare the incidence of CVD events and mortality among the cohorts and analyze the interactions.ResultsThe subjects comprised of 6696 women and 5819 men who completed a mean 8.8-year follow-up. All of the participants had two or more CVD risk factors at baseline. At the end of the study, 4049 CVD events had occurred in 2304 participants. The incidence of non-fatal CVD events was 14.08, 16,65, and 25.21 % in the low-NC, medium-NC, and high-NC cohorts, respectively (p < 0.001). The all-cause mortality was 9.77, 11.93, and 19.31 %, and CVD mortality, 4.00, 6.29, and 8.01 %, respectively (p < 0.001). Compared with baseline, the number of CVD risk factors in participants had increased from 2.6, 3.0, and 3.4 to 3.5, 4.1, and 4.7 in the low-, medium-, and high-NC cohorts (34, 36, and 38 %), respectively. The event-free survival rate was 95.32, 80.15, and 75.47 %, respectively.ConclusionsA higher NC indicated a higher incidence of future fatal and non-fatal CVD events and all-cause mortality in both male and female high-risk participants. CVD risk factors increased more in the higher NC group. NC as a novel indicator of CVD showed good predictive ability for CVD events and mortality in a high-risk population.

Traditional macroscopic and microscopic identification methods of medicinal materials are economical and practical, but usually experience-based due to few chemical supports. Here histochemical evaluation on bioactive components of Coptidis Rhizoma (CR) in anatomic sections using laser microdissection and liquid chromatography-mass spectrometry (LMD-LC-MS) was developed to correlate the inner quality and outer features of materials from different growing areas. Results of a total 33 peaks representing potential different alkaloids were detected and 8 common peaks were identified as the major alkaloids, namely magnoflorine, thalifendine, columbamine, epiberberine, jatrorrhizine, coptisine, palmatine, and berberine. Six major alkaloids were quantified in the top and middle sections of raw materials and in their tissues and cells at the same time. Histochemical analyses showed consistent results with direct determination in raw materials and explained the reason why top sections of all samples contained higher contents of alkaloids by giving out attributions of each alkaloid in different anatomic sections. Besides, results manifested the distribution and accumulation rules of alkaloids in diverse tissues and cells of CR. This study demonstrates an effective and scientific way to correlate bioactive components and morphological features of medicinal materials, which is beneficial to future research, agriculture and application.

This study aimed to identify key genes involved in the progression of diabetic pancreatic ductal adenocarcinoma (PDAC). Two gene expression datasets (GSE74629 and GSE15932) were obtained from Gene Expression Omnibus. Then, differentially expressed genes (DEGs) between diabetic PDAC and non-diabetic PDAC were identified, followed by a functional analysis. Subsequently, gene modules related to DM were extracted by weighed gene co-expression network analysis. The protein-protein interaction (PPI) network for genes in significant modules was constructed and functional analyses were also performed. After that, the optimal feature genes were screened by support vector machine (SVM) recursive feature elimination and SVM classification model was built. Finally, survival analysis was conducted to identify prognostic genes. The correlations between prognostic genes and other clinical factors were also analyzed. Totally, 1546 DEGs with consistent change tendencies were identified and functional analyses showed they were strongly correlated with metabolic pathways. Furthermore, there were two significant gene modules, in which RPS27A and UBA52 were key genes. Functional analysis of genes in two gene modules revealed that these genes primarily participated in oxidative phosphorylation pathway. Additionally, 21 feature genes were closely related with diabetic PDAC and the corresponding SVM classifier markedly distinguished diabetic PDAC from non-diabetic PDAC patients. Finally, decreased KIF22 and PYGL levels had good survival outcomes for PDAC. Four genes (RPS27A, UBA52, KIF22 and PYGL) might be involved in the pathogenesis of diabetic PDAC. Furthermore, KIF22 and PYGL acted as prognostic biomarkers for diabetic PDAC.

Numerous drugs and compounds have been validated as protecting against myocardial ischemia (MI), a leading cause of heart failure; however, synergistic possibilities among them have not been systematically explored. Thus, there appears to be significant room for optimization in the field of drug combination therapy for MI. Here, we propose an easy approach for the identification and optimization of MI-related synergistic drug combinations via visualization of the crosstalk between networks of drug targets corresponding to different drugs (each drug has a unique network of targets). As an example, in the present study, 28 target crosstalk networks (TCNs) of random pairwise combinations of 8 MI-related drugs (curcumin, capsaicin, celecoxib, raloxifene, silibinin, sulforaphane, tacrolimus, and tamoxifen) were established to illustrate the proposed method. The TCNs revealed a high likelihood of synergy between curcumin and the other drugs, which was confirmed by in vitro experiments. Further drug combination optimization showed a synergistic protective effect of curcumin, celecoxib, and sililinin in combination against H2O2-induced ischemic injury of cardiomyocytes at a relatively low concentration of 500 nM. This result is in agreement with the earlier finding of a denser and modular functional crosstalk between their networks of targets in the regulation of cell apoptosis. Our study offers a simple approach to rapidly search for and optimize potent synergistic drug combinations, which can be used for identifying better MI therapeutic strategies. Some new light was also shed on the characteristic features of drug synergy, suggesting that it is possible to apply this method to other complex human diseases.

Background: This study sought to identify candidate biomarkers associated with gastric cancer (GC) prognosis based on an integrated bioinformatics analysis.Methods: First, the GSE54129 and GSE79973 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) identified between the 2 data sets were screened using the limma software package in R, and the intersection DEGs were obtained by a Venn analysis. Subsequently, gene clustering and a functional analysis were performed to explore the roles of the DEGs. The protein-protein interaction (PPI) network of the genes in clusters was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins. A survival analysis evaluated the associations between the candidate genes and the overall survival of GC patients. A drug-gene interaction analysis and an external data set analysis were conducted using The Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) data set to validate the prognostic genes.Results: We extracted 421 intersection DEGs from the 2 GEO data sets. There were 5 gene clusters, and the functional analysis revealed that they were mainly associated with the extracellular matrix-receptor interaction pathway. The PPI interaction analysis identified the top 36 hub genes. The survival analysis revealed that 7 upregulated genes [i.e., platelet-derived growth factor receptor beta (PDGFRB), angiopoietin 2 (ANGPT2), vascular endothelial growth factor C (VEGFC), collagen type IV alpha 2 chain (COL4A2), collagen type IV alpha 1 chain (COL4A1), thrombospondin 1 (THBS1), and fibronectin 1 (FN1)] were associated with the survival prognosis of GC patients. The 20 drug-gene interaction pairs among the 4 genes and 18 drugs were obtained. Finally, TCGA-STAD data set was used to validate the expression levels of COL4A1, PDGFRB, and FN1.Conclusions: We found that 7 upregulated genes (i.e., PDGFRB, ANGPT2, VEGFC, COL4A2, COL4A1, THBS1, and FN1) were promising markers of prognosis in GC patients.