NADPH oxidase 4 mediates insulin-stimulated HIF-1α and VEGF expression, and angiogenesis in vitro

Acute intensive insulin therapy causes a transient worsening of diabetic retinopathy in type 1 diabetes patients and is related to VEGF expression. Reactive oxygen species (ROS) have been shown to be involved in HIF-1α and VEGF expression induced by insulin, but the role of specific ROS sources has not been fully elucidated. In this study we examined the role of NADPH oxidase subunit 4 (Nox4) in insulin-stimulated HIF-1α and VEGF expression, and angiogenic responses in human microvascular endothelial cells (HMVECs). Here we demonstrate that knockdown of Nox4 by siRNA reduced insulin-stimulated ROS generation, the tyrosine phosphorylation of IR-β and IRS-1, but did not change the serine phosphorylation of IRS-1. Nox4 gene silencing had a much greater inhibitory effect on insulin-induced AKT activation than ERK1/2 activation, whereas it had little effect on the expression of the phosphatases such as MKP-1 and SHIP. Inhibition of Nox4 expression inhibited the transcriptional activity of VEGF through HIF-1. Overexpression of wild-type Nox4 was sufficient to increase VEGF transcriptional activity, and further enhanced insulin-stimulated the activation of VEGF. Downregulation of Nox4 expression decreased insulin-stimulated mRNA and protein expression of HIF-1α, but did not change the rate of HIF-1α degradation. Inhibition of Nox4 impaired insulin-stimulated VEGF expression, cell migration, cell proliferation, and tube formation in HMVECs. Our data indicate that Nox4-derived ROS are essential for HIF-1α-dependent VEGF expression, and angiogenesis in vitro induced by insulin. Nox4 may be an attractive therapeutic target for diabetic retinopathy caused by intensive insulin treatment

Synergistic strategy with hyperthermia therapy based immunotherapy and engineered exosomes−liposomes targeted chemotherapy prevents tumor recurrence and metastasis in advanced breast cancer

Advanced breast cancer with recurrent and distal organ metastasis is aggressive and incurable. The current existing treatment strategies for advanced breast cancer are difficult to achieve synergistic treatment of recurrent tumors and distant metastasis, resulting in poor clinical outcomes. Herein, a synergistic therapy strategy composed of biomimetic tumor-derived exosomes (TEX)-Liposome-paclitaxel (PTX) with lung homing properties and gold nanorods (GNR)-PEG, was designed, respectively. GNR-PEG, with well biocompatibility, cured recurrent tumors effectively by thermal ablation under the in situ NIR irradiation. Meanwhile, GNR-mediated thermal ablation activated the adaptive antitumor immune response, significantly increased the level of CD8+ T cells in lungs and the concentration of serum cytokines (tumor necrosis factor-α, interlekin-6, and interferon-γ). Subsequently, TEX-Liposome-PTX preferentially accumulated in lung tissues due to autologous tumor-derived TEX with inherent specific affinity to lung, resulting in a better therapeutic effect on lung metastasis tumors with the assistance of adaptive immunotherapy triggered by GNR in vivo. The enhanced therapeutic efficacy in advanced breast cancer was a combination of thermal ablation, adaptive antitumor immunotherapy, and targeted PTX chemotherapy. Hence, the synergistic strategy based on GNR and TEX-Liposome provides selectivity to clinical treatment of advanced breast cancer with recurrent and metastasis

The analyses of risk factors for COPD in the Li ethnic group in Hainan, People’s Republic of China

OBJECTIVE: To study the risk factors for chronic obstructive pulmonary disease (COPD) in Li population in Hainan province, People’s Republic of China. METHODS: Li people above 40 years of age from Hainan were chosen by stratified random cluster sampling between 2012 and 2014. All participants were interviewed with a home-visiting questionnaire, and spirometry was performed on all eligible participants. Patients with airflow limitation (forced expiratory volume in 1 second [FEV(1)]/forced vital capacity [FVC] <0.70) were further examined by postbronchodilator spirometry, and those with a postbronchodilator FEV(1)/FVC <0.70 was diagnosed with COPD. The information of physical condition and history, smoking intensity, smoking duration, second-hand smoking, education, job category, monthly household income, working years, residential environment, primary fuel for cooking and heating (biomass fuel including wood, crop residues, dung, and charcoal, or modern fuel such as natural gas, liquefied petroleum gas, electricity, and solar energy), ventilated kitchen, heating methods, air pollution, recurrent respiratory infections, family history of respiratory diseases, cough incentives, and allergies of COPD and non-COPD subjects was analyzed by univariate and multivariate logistic regression models to identify correlated risk factors for COPD. RESULTS: Out of the 5,463 Li participants, a total of 277 COPD cases were identified by spirometry, and 307 healthy subjects were randomly selected as controls. Univariate logistic regression analyses showed that older people (65 years and above), low body mass index (BMI), biomass smoke, 11–20 and >20 cigarettes/day, smoking for 40 years or more, second-hand smoking, recurrent respiratory infections, and induced cough were risk factors for COPD, whereas high BMI, high education level, and presence of ventilated kitchen were protective factors. Subsequent multivariate logistic regression model further demonstrated that aging, low BMI, biomass smoke, >20 cigarettes/day, and recurrent respiratory tract infections were high-risk factors for COPD in the Li population. CONCLUSION: The incidence of COPD has a strong correlation with age, BMI, biomass smoke, >20 cigarettes/day, and recurrent respiratory infections, suggesting they were high-risk factors for COPD in Li population

Mendelian randomization and Bayesian model averaging of autoimmune diseases and Long COVID

BackgroundFollowing COVID-19, reports suggest Long COVID and autoimmune diseases (AIDs) in infected individuals. However, bidirectional causal effects between Long COVID and AIDs, which may help to prevent diseases, have not been fully investigated.MethodsSummary-level data from genome-wide association studies (GWAS) of Long COVID (N = 52615) and AIDs including inflammatory bowel disease (IBD) (N = 377277), Crohn’s disease (CD) (N = 361508), ulcerative colitis (UC) (N = 376564), etc. were employed. Bidirectional causal effects were gauged between AIDs and Long COVID by exploiting Mendelian randomization (MR) and Bayesian model averaging (BMA).ResultsThe evidence of causal effects of IBD (OR = 1.06, 95% CI = 1.00–1.11, p = 3.13E-02), CD (OR = 1.10, 95% CI = 1.01–1.19, p = 2.21E-02) and UC (OR = 1.08, 95% CI = 1.03–1.13, p = 2.35E-03) on Long COVID was found. In MR-BMA, UC was estimated as the highest-ranked causal factor (MIP = 0.488, MACE = 0.035), followed by IBD and CD.ConclusionThis MR study found that IBD, CD and UC had causal effects on Long COVID, which suggests a necessity to screen high-risk populations

A service encapsulation method of NC machine tool resources in cloud manufacturing

Abstract NC machine tool resources are important manufacturing resources in cloud manufacturing. How to carry out semantic description and service encapsulation of heterogeneous, decentralized and autonomous NC machine tool resources is one of the key technologies to realize the cloud manufacturing mode to the workshop level. In order to solve the problems such as encapsulation complexity and lack of specification in the process of service encapsulation of NC machine tool resources in cloud manufacturing, this paper first analyzes the basic attributes, functional attributes and other attributes of NC machine tool resources in cloud manufacturing. Then, a semantic description method of NC machine tool resources based on XML language is proposed. Finally, a service encapsulation method of NC machine tool resources based on JAX-WS is put forward.</jats:p

Experimental performance of deep learning channel estimation for an X-ray communication-based OFDM-PWM system

A deep learning channel estimation scheme in orthogonal frequency division multiplexing for X-ray communication (XCOM) is studied. The scheme uses simulated and detected data obtained with different working parameters and numbers of pilots as training and testing data, respectively, for the deep neural network (DNN) model. The bit-error-rate performance of the DNN model under various system operating parameters, numbers of pilot sequences, and channel obstructions is investigated separately. Experiment results showed that the deep-learning-based approach can address the distortion of the air-scintillator channel for XCOM, giving a performance comparable to those of least-squares and minimum-mean-square error estimation methods.</jats:p