Background Cardiovascular disease (CVD) was in common in Coronavirus Disease 2019 (COVID-19) patients and associated with unfavorable outcomes. We aimed to compare the clinical observations and outcomes of SARS-CoV-2-infected patients with or without CVD. Methods Patients with laboratory-confirmed SARS-CoV-2 infection were clinically evaluated at Wuhan Seventh People’s Hospital, Wuhan, China, from January 23 to March 14, 2020. Demographic data, laboratory findings, comorbidities, treatments and outcomes were collected and analyzed in COVID-19 patients with and without CVD. Results Among 596 patients with COVID-19, 215 (36.1%) of them with CVD. Compared with patients without CVD, these patients were significantly older (66 years vs 52 years) and had higher proportion of men (52.5% vs 43.8%). Complications in the course of disease were more common in patients with CVD, included acute respiratory distress syndrome (22.8% vs 8.1%), malignant arrhythmias (3.7% vs 1.0%) including ventricular tachycardia/ventricular fibrillation, acute coagulopathy(7.9% vs 1.8%), and acute kidney injury(11.6% vs 3.4%). The rate of glucocorticoid therapy (36.7% vs 25.5%), Vitamin C (23.3% vs 11.8%), mechanical ventilation (21.9% vs 7.6%), intensive care unit admission (12.6% vs 3.7%) and mortality (16.7% vs 4.7%) were higher in patients with CVD (both p < 0.05). The multivariable Cox regression models showed that older age (≥65 years old) (HR 3.165, 95%CI 1.722-5.817) and patients with CVD (HR 2.166, 95%CI 1.189-3.948) were independent risk factors for death. Conclusions CVD are independent risk factors for COVID-19 patients. COVID-19 patients with CVD were more severe and had higher mortality rate, early intervention and vigilance should be taken.

CpG islands (CGIs) are CpG-rich regions compared to CpG-depleted bulk DNA of mammalian genomes and are generally regarded as the epigenetic regulatory regions in association with unmethylation, promoter activity and histone modifications. Accurate identification of CpG islands with epigenetic regulatory function in bulk genomes is of wide interest. Here, the common features of functional CGIs are identified using an average mutual information method to differentiate functional CGIs from the remaining CGIs. A new approach (CpG mutual information, CpG_MI) was further explored to identify functional CGIs based on the cumulative mutual information of physical distances between two neighboring CpGs. Compared to current approaches, CpG_MI achieved the highest prediction accuracy. This approach also identified new functional CGIs overlapping with gene promoter regions which were missed by other algorithms. Nearly all CGIs identified by CpG_MI overlapped with histone modification marks. CpG_MI could also be used to identify potential functional CGIs in other mammalian genomes, as the CpG dinucleotide contents and cumulative mutual information distributions are almost the same among six mammalian genomes in our analysis. It is a reliable quantitative tool for the identification of functional CGIs from bulk genomes and helps in understanding the relationships between genomic functional elements and epigenomic modifications.

Bladder cancer (BC) is one of the most malignancies in terms of incidence and recurrence worldwide. The aim of this study is to find out novel and prognostic biomarkers for patients with BC. First, we identified 258 differentially expressed genes by using GSE19915 from Gene Expression Omnibus database. Second, a total of 33 modules were identified by constructing a coexpression network by using weighted gene coexpression network analysis and yellow module was regarded as the key module. Furthermore, by constructing protein–protein interaction networks, we preliminarily picked out 13 genes. Among them, four hub genes (CCNB1, KIF4A, TPX2, and TRIP13) were eventually identified by using five different methods (survival analysis, one‐way analysis of variance, the Spearman correlation analysis, receiver operating characteristic curve, and expression value comparison), which were significantly correlated with the prognosis of BC. The validation of transcriptional and translational levels made sense (based on Oncomine and The Human Protein Atlas database). Moreover, functional enrichment analysis suggested that all the hub genes played crucial roles in chromosome segregation, sister chromatid segregation, nuclear chromosome segregation, mitotic nuclear division, nuclear division, and organelle fission during cell mitosis. In addition, three of the hub genes (KIF4A, TPX2, and TRIP13) might be potential targets of cancer drugs according to the results of the genetical alteration. In conclusion, this study indicates that four hub genes have great predictive value for the prognosis of BC, and may contribute to the exploration of the further and more in‐depth research of BC.

This study was to evaluate the expression of miR-129–5p in non-alcoholic fatty liver (NAFLD) patients and its clinical value and explore its regulatory effect on insulin resistance (IR). A total of 117 NAFLD patients and 110 healthy controls were included. The levels of miR-129-5p were detected by qRT-PCR. To assess the diagnostic value of miR-129-5p for NAFLD, the receiver operating characteristic curve (ROC) was established. C57Bl/6 mice were supplied with high-fat diet to establish NAFLD model. Intraperitoneal insulin tolerance test (IPITT) was carried out to evaluate the effect of miR-129-5p on IR in NAFLD animal model. miR-129-5p was highly expressed in the serum of NAFLD patients, and patients with HOMA-IR ≥2.5 had higher level of miR-129-5p than those with HOMA-IR <2.5. miR-129-5p had the ability to differentiate NAFLD patients from healthy individuals and might be associated with the development of IR. Serum miR-129-5p was positively correlated with the levels of HOMA-IR, BMI, total cholesterol (TC), and triglyceride (TG) in NAFLD patients. Downregulation of miR-129-5p regulates lipid metabolism and insulin sensitivity in NAFLD mice model. MiR-129-5p was upregulated in NAFLD patients and might be a potential diagnostic biomarker. The regulatory effect of miR-129-5p on NAFLD may function by regulating lipid accumulation and insulin sensitivity.

ObjectiveDyslipidemia is a key risk factor for coronary artery disease (CAD). This study aimed to investigate the correlation between the atherogenic index of plasma (AIP) and the severity of CAD.Methods2,491 patients were enrolled in this study and analyzed retrospectively, including 665 non-CAD patients as the control group and 1,826 CAD patients. The CAD patients were classified into three subgroups according to tertiles of SYNTAX score (SS). Non-high-density lipoprotein cholesterol (Non-HDL-C) was defined as serum total cholesterol (TC) minus serum high-density lipoprotein cholesterol (Non-HDL-C), atherogenic index (AI) was defined as the ratio of non-HDL-C to HDL-C; AIP was defined as the logarithm of the ratio of the concentration of triglyceride (TG) to HDL-C; lipoprotein combine index (LCI) was defined as the ratio of TC∗TG∗ low-density lipoprotein cholesterol (LDL)to HDL-C; Castelli Risk Index I (CRI I) was defined as the ratio of TC to HDL-C; Castelli Risk Index II (CRI II) was defined as the ratio of LDL-C to HDL-C.ResultsThe levels of AIP (P &lt; 0.001), AI (P &lt; 0.001), and LCI (P = 0.013) were higher in the CAD group compared with the non-CAD group. The Spearman correlation analysis showed that AIP (r = 0.075, P &lt; 0.001), AI (r = 0.132, P &lt; 0.001), and LCI (r = 0.072, P = 0.001) were positively correlated with SS. The multivariate logistic regression model showed CRI I (OR: 1.11, 95% CI: 1.03–1.19, P = 0.005), CRI II (OR: 1.26, 95% CI: 1.15–1.39, P &lt; 0.001), AI (OR: 1.28, 95% CI: 1.17–1.40, P &lt; 0.001), AIP (OR: 2.06, 95% CI: 1.38–3.07, P &lt; 0.001), and LCI (OR: 1.01, 95% CI: 1.01–1.02, P &lt; 0.001) were independent predictors of severity of CAD After adjusting various confounders.ConclusionCRI I, CRI II, AIP, AI, and LCI were independent predictors of the severity of CAD, which could be used as a biomarker for the evaluation of the severity of CAD.