Background: Exposure to air pollution is associated with chronic obstructive pulmonary disease (COPD). However, findings on the effects of air pollution on lung function and systemic inflammation in Chinese COPD patients are inconsistent and scarce. This study aims to evaluate the effects of ambient air pollution on lung function parameters and serum cytokine levels in a COPD cohort in Beijing, China. Methods: We enrolled COPD participants on a rolling basis from December 2015 to September 2017 in Beijing, China. Follow-ups were performed every 3 months for each participant. Serum levels of 20 cytokines were detected every 6 months. Hourly ambient pollutant levels over the same periods were obtained from 35 monitoring stations across Beijing. Geocoded residential addresses of the participants were used to estimate daily mean pollution exposures. A linear mixed-effect model was applied to explore the effects of air pollutants on health in the first-year of follow-up. Results: A total of 84 COPD patients were enrolled at baseline. Of those, 75 COPD patients completed the first-year of follow-up. We found adverse cumulative effects of particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and carbon monoxide (CO) on the forced vital capacity % predicted (FVC % pred) in patients with COPD. Further analyses illustrated that among COPD patients, air pollution exposure was associated with reduced levels of serum eotaxin, interleukin 4 (IL-4) and IL-13 and was correlated with increased serum IL-2, IL-12, IL-17A, interferon γ (IFNγ), monocyte displacing protein 1 (MCP-1) and soluble CD40 ligand (sCD40L). Conclusion: Acute exposures to PM_2.5, NO₂, SO₂ and CO were associated with a reduction in FVC % pred in COPD patients. Furthermore, short-term exposure to air pollutants increased systemic inflammation in COPD patients; this may be attributed to increased Th1 and Th17 cytokines and decreased Th2 cytokines.

Purpose: Evidence between air pollution and COPD admissions is inconsistent and limited in China. In this study, we aimed to explore the effects of air pollutants on COPD admissions in Beijing, China. Patients and methods: Daily COPD hospital admission visits derived from tertiary and secondary hospitals in Beijing were retrieved from January 2013 to February 2017. Air pollutant levels and meteorological data over the same periods were also achieved. Generalized additive model was applied to estimate the percentage changes with 95% CIs in daily admissions corresponding to 10 µg/m³ increases in pollutants levels [1 mg/m³ in carbon monoxide (CO)], stratified by age, gender, and season. Results: Seventy-three thousand seventy-six COPD hospital admission visits were included with mean daily visits of 48 (21). Cumulative lag effect with per 10 µg/m³ increase in air pollutant levels was largest for nitrogen dioxide (NO₂) with 3.03% (95% CI: 1.82%–4.26%) at lag 06, for sulfur dioxide (SO₂) with 2.07% (95% CI: 1.00%–3.15%) at lag 01, for particulate matter ≤10 µm in aerodynamic diameter (PM₁₀) with 0.92% (95% CI: 0.55%–1.30%) at lag 07, and for particulate matter ≤2.5 µm in aerodynamic diameter (PM_2.5) with 0.82% (95% CI: 0.38%–1.26%) at lag 06, respectively. Percentage increase for each 1 mg/m³ increase in CO was 5.99% (95% CI: 2.74%–9.34%) at lag 06. Further, stronger effects on COPD admissions were found in warm seasons than in cold seasons. Conclusion: Short-term exposures to PM_2.5, PM₁₀, NO₂, SO₂, and CO had adverse effects on COPD hospitalizations in Beijing with different magnitudes and lag days.

Background: Alzheimer's disease (AD) is the most common type of dementia. Scientists have discovered that the causes of AD may include a combination of genetic, lifestyle, and environmental factors, but the exact cause has not yet been elucidated. Effective strategies to prevent and treat AD therefore remain elusive. The identified genetic causes of AD mainly focus on individual genes, but growing evidence has shown that complex diseases are usually affected by the interaction of genes in a network. Few studies have focused on the interactions and correlations between genes and how they are gradually destroyed or disappear during AD progression. A differential network analysis has been recognized as an essential tool for identifying the underlying pathogenic mechanisms and significant genes for prediction analysis. We therefore aim to conduct a differential network analysis to reveal potential networks involved in the neuropathogenesis of AD and identify genes for AD prediction. Methods: In this paper, we selected 365 samples from the Religious Orders Study and the Rush Memory and Aging Project, including 193 clinically and neuropathologically confirmed AD subjects and 172 no cognitive impairment (NCI) controls. Then, we selected 158 genes belonging to the AD pathway (hsa05010) of the Kyoto Encyclopedia of Genes and Genomes. We employed a machine learning method, namely, joint density-based non-parametric differential interaction network analysis and classification (JDINAC), in the analysis of gene expression data (RNA-seq data). We searched for the differential networks in the RNA-seq data with a pathological diagnosis of AD. Finally, an optimal prediction model was built through cross-validation, which showed good discrimination and calibration for AD prediction. Results: We used JDINAC to derive a gene co-expression network and to explore the relationship between the interaction of gene pairs and AD, and the top 10 differential gene pairs were identified. We then compared the prediction performance between JDINAC and individual genes based on prediction methods. JDINAC provides better accuracy of classification than the latest methods, such as random forest and penalized logistic regression. Conclusions: The interaction between gene pairs is related to AD and can provide more insight than the individual genes in AD prediction.

In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. The simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods. Although regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables.

The prevalence of cardiovascular disease has been increasing worldwide. As a common pathogenic risk factor, dyslipidemia played a great role in the incidence and progress of these diseases. We investigated to achieve accurate and up-to-date information on the prevalence of dyslipidemia and its associations with other lipid-related diseases in rural North China. Using a complex, multistage, probability sampling design, we conducted a large-scale cross-sectional study of 8528 rural participants aged over 18 years in Shandong Province. Prevalence and characteristics of dyslipidemia were demonstrated. The odds ratios between dyslipidemia types and lipid-related diseases were further analyzed by logistic regression. Among the overall population, 45.8 % suffered from dyslipidemia. The prevalence of lipid abnormality (including high and very high levels) was 18.6, 12.7, 9.8 and 12.7 % for total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) cholesterol and triglycerides (TG), respectively. Among all participants with dyslipidemia, 23.9 % were aware, only 11.5 % were treated, 10.0 % were controlled. For subjects with dyslipidemia, the risk for non-alcoholic fatty liver disease (NAFLD) was highest with a 3.3-fold over that of non-dyslipidmia (OR = 3.30, P < 0.001); followed by hyperuricemia and diabetes mellitus (DM), while with 2-fold increase (OR = 1.99, P < 0.001; OR = 1.92, P < 0.001); with only 1.5-fold risk for atherosclerosis (AS) (OR = 1.47, P < 0.001). The presence of high cholesterol was mainly associated with AS, while abnormal TG was correlated with NAFLD and DM. Dyslipidemia has become a serious public health issue in rural North China. The rapid increase of high TC and incremental risk of high TG may contribute to the epidemic of AS, NAFLD and DM. It is imperative to develop individualized prevention and treatment guidelines according to dyslipidemia phenotypes.

Background: With the rapid development and wide application of high-throughput sequencing technology, biomedical research has entered the era of large-scale omics data. We aim to identify genes associated with breast cancer prognosis by integrating multi-omics data.Method: Gene-gene interactions were taken into account, and we applied two differential network methods JDINAC and LGCDG to identify differential genes. The patients were divided into case and control groups according to their survival time. The TCGA and METABRIC database were used as the training and validation set respectively.Result: In the TCGA dataset, C11orf1, OLA1, RPL31, SPDL1 and IL33 were identified to be associated with prognosis of breast cancer. In the METABRIC database, ZNF273, ZBTB37, TRIM52, TSGA10, ZNF727, TRAF2, TSPAN17, USP28 and ZNF519 were identified as hub genes. In addition, RPL31, TMEM163 and ZNF273 were screened out in both datasets. GO enrichment analysis shows that most of these hub genes were involved in zinc ion binding.Conclusion: In this study, a total of 15 hub genes associated with long-term survival of breast cancer were identified, which can promote understanding of the molecular mechanism of breast cancer and provide new insight into clinical research and treatment.