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Background. Patients with liver cirrhosis have a high risk of sepsis and a poor prognosis. Recently, a new standard for sepsis (Sepsis-3) has been proposed in the general population. The Coulter Lh 750 hematology analyzer can evaluate mean volume, conductivity, scatter, and distribution width of leukocyte. We tried to use Sepsis-3 criteria to study the diagnostic value of volume, conductivity, and scattering (VCS) parameters in sepsis and infection in patients with liver cirrhosis compared with traditional infection markers (PCT, IL-6, sCDl63). Methods. A blinded, cohort study was conducted in three different ED populations within three affiliated hospitals. A total of 249 patients with liver cirrhosis were enrolled in the study. According to the "Sepsis-3" consensus criteria, clinical history, and laboratory examination, the subjects were divided into sepsis (n=54), patients with infections (n=95), and patients without systemic infections (n=100). The blood samples of the patients were collected at the time of ED admission and were evaluated for the detection of sepsis. Results. The differences of MNV, MNS, MMV, MMS, MLV, NDW, and MDW in the three groups were statistically significant. In the diagnosis of sepsis in patients with liver cirrhosis, the sensitivity of combined detection of MMV and MDW was 88.89%; the specificity was 74%. This sensitivity was significantly better than the 83.3% achieved using 0.97 mg/L as the cutoff for sCD163. In the diagnosis of infection in cirrhosis, the sensitivity of combination of MNV and MMS was increased to 86.32%; the specificity was 92%. The sensitivity was the same as that achieved by using 0.31 ng/mL as the cutoff value of PCT, but the specificity increased. Conclusion. The leukocyte VCS parameter could be potential parameters for indicating sepsis and infection in patients with liver cirrhosis. The combined detection of MMV and MDW seemed to be helpful for the diagnosis of sepsis in these patients, and the combination of MNV and MMS could better indicate infection for them. [ABSTRACT FROM AUTHOR]

Objectives. Schizophrenia (SCZ) is associated with disrupted functional brain connectivity, and antipsychotic medications are the primary and most commonly used treatment for schizophrenia. However, not all patients respond to antipsychotic medications. Methods. The study is aimed at investigating whether the graph-theory-based degree centrality (DC), derived from resting-state functional MRI (rs-fMRI), can predict the treatment outcomes. rs-fMRI data from 38 SCZ patients were collected and compared with findings from 38 age- and gender-matched healthy controls (HCs). The patients were treated with antipsychotic medications for 16 weeks before undergoing a second rs-fMRI scan. DC data were processed using DPABI and SPM12 software. Results. SCZ patients at baseline showed increased DC in the frontal and temporal gyrus, anterior cingulate cortex, and precuneus and reduced DC in bilateral subcortical gray matter structures. However, those abnormalities showed a clear renormalization after antipsychotic medication treatments. Support vector machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.2% (sensitivity 78.9%, specificity 89.5%, and area under the receiver operating characteristic curve (AUC) 0.925) for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the bilateral putamen, left inferior frontal gyrus, left middle occipital cortex, bilateral middle frontal gyrus, left cerebellum, left medial frontal gyrus, left inferior temporal gyrus, and left angular. Furthermore, the DC change within the bilateral putamen is negatively correlated with the symptom improvements after treatment. Conclusions. Our study confirmed that graph-theory-based measures, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of antipsychotic medications. [ABSTRACT FROM AUTHOR]

Background. Coronary artery disease (CAD) is a type of heart disease with a high morbidity rate. This study is aimed at identifying potential biomarkers closely related to the progression of CAD. Materials and Methods. A microarray dataset of GSE59867 was downloaded from a public database, Gene Expression Omnibus, which included 46 cases of stable CAD without a history of myocardial infarction (MI), 30 cases of MI without heart failure (HF), and 34 cases of MI with HF. Differentially expressed long noncoding RNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified by the limma package, and functions of DEmRNAs were annotated by Gene Ontology and KEGG pathways. In addition, weighed gene coexpression network analysis (WGCNA) was used to construct a coexpression network of DEmRNAs, and a disease-related lncRNAs-mRNAs-pathway network was constructed. Finally, the datasets of GSE61145 and GSE57338 were used to verify the expression levels of the above highly correlated candidates. Results. A total of 2362 upregulated mRNAs and 2816 downregulated mRNAs, as well as 235 upregulated lncRNAs and 113 downregulated lncRNAs were screened. These genes were significantly enriched in "cytokine-cytokine receptor interaction," "RIG-I-like receptor signaling pathway," and "natural killer cell-mediated cytotoxicity." Five modules including 1201 DEmRNAs were enriched in WGCNA. A coexpression network including 19 DElncRNAs and 413 DEmRNAs was constructed. These genes were significantly enriched in "phosphatidylinositol signaling system," "insulin signaling pathway," and "MAPK signaling pathway". Disease-related gene-pathway network suggested FASN in "insulin signaling pathway," DGKZ in "phosphatidylinositol signaling system," and TNFRSF1A in "MAPK signaling pathway" were involved in MI. Conclusion. FASN, DGKZ, and TNFRSF1A were revealed to be CAD progression-associated genes by WGCNA coexpression network analysis. [ABSTRACT FROM AUTHOR]