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Mesenchymal stem cells (MSCs) are a kind of adult stem cells that can be isolated easily from bone marrow, adipose tissue, umbilical cord and many other tissues. MSCs have been shown to specifically migrate to inflammatory sites, including tumors, and hold great promise as tumor-specific vectors to deliver antitumor agents. Interferon-α (IFNα) has been used in clinic to treat various types of tumors; however, because of its short half-life, significant therapeutic effects require high doses that often results in serious side effects. Here, we tested whether MSCs continuingly secreting IFNα can exert a persistent antitumor effect and eliminate the side effects associated with high clinical doses of recombinant IFNα. We found that even a small number of IFNα-secreting MSCs could potently halt B16 tumor growth in vivo. The antitumor activity of IFNα-secreting MSCs was largely abolished in immunodeficient mice, an effect largely attributed to natural killer cells and CD8(+) T cells. Therefore, IFNα-secreting MSCs provide an innovative strategy for tumor therapy.

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Background and AimsThe association between gallstone disease and coronary artery atherosclerotic disease (CAD) remains unclear. To clarify their relationship, patients with CAD newly diagnosed by coronary angiography were investigated in this cross-sectional study.MethodsThe study cohort consisted of 1,270 patients undergoing coronary angiography for the first time between January 2007 and September 2011. Patients with ≥50% diameter stenosis in any major coronary artery on coronary angiography were defined as being CAD positive (n = 766) and those with no stenosis as CAD negative (n = 504). Multivariate logistic regression was used to investigate the relationship between gallstone disease and CAD. The odds ratios (OR) of factors associated with CAD were calculated. In addition, CAD-positive and CAD-negative patients were matched one-to-one by age, gender and metabolic syndrome (MetS), and the association between gallbladder disease and CAD was determined.ResultsThe prevalence of gallstone disease was significantly higher in CAD-positive than in CAD negative patients (149/766 [19.5%] vs 57/504 [11.3%], P<0.01). Gallstone disease was significantly associated with CAD (adjusted OR = 1.59, 95% confidence interval [CI] 1.10–2.31). Following matched pairing of 320 patients per group, gallstone disease remained significantly associated with CAD (adjusted OR = 1.69, 95% CI: 1.08–2.65).ConclusionGallstone disease is strongly associated with CAD diagnosed by coronary angiography.

BackgroundFatty liver index (FLI) was recently established to predict non-alcoholic fatty liver disease (NAFLD) in general population, which is known to be associated with coronary artery atherosclerotic disease (CAD).This study aims to investigate whether FLI correlates with NAFLD and with newly diagnosed CAD in a special Chinese population who underwent coronary angiography.MethodsPatients with CAD (n = 231) and without CAD (n = 482) as confirmed by coronary angiography were included. Among them, 574 patients underwent B-ultrosonography were divided into NAFLD group (n = 209) and non-NAFLD group (n = 365). Correlation between FLI and NAFLD was analyzed using pearson’s correlation. The associations between FLI and NAFLD as well as CAD were assessed using logistic regression. The predictive accuracy of FLI for NAFLD was evaluated using receiver operating characteristics (ROC) curve analysis.ResultsFLI was significantly higher in NAFLD group (37.10 ± 1.95) than in non-NAFLD group (17.70 ± 1.04), P < 0.01. FLI correlated with NAFLD (r = 0.372, P < 0.001). The algorithm for FLI had a ROC-AUC of 0.721 (95% CI: 0.678–0.764) in the prediction of NAFLD. Logistic regression analysis showed that FLI was associated with NAFLD (adjusted OR = 1.038, 95% CI: 1.029-1.047, P < 0.01). The proportion of patients with CAD did not differ among the groups of FLI ≤ 30 (32.3%), 30-60 (31.0%), and ≥60 (35.3%). No significant association was found between FLI and CAD (adjusted OR = 0.992, 95% CI: 0.981-1.003 in men and OR = 0.987, 95% CI: 0.963-1.012 in women, P > 0.05).ConclusionsFLI showed good correlation with NAFLD in patients who underwent coronary angiography, but not with newly diagnosed CAD. This might be underestimated because some patients in non-CAD group may have other underlying cardiovascular diseases.

High-speed railways have been one of the most popular means of transportation all over the world. As an important part of the high-speed railway power supply system, the overhead catenary system (OCS) directly influences the stable operation of the railway, so regular inspection and maintenance are essential. Now manual inspection is too inefficient and high-cost to fit the requirements for high-speed railway operation, and automatic inspection becomes a trend. The 3D information in the point cloud is useful for geometric parameter measurement in the catenary inspection. Thus it is significant to recognize the components of OCS from the point cloud data collected by the inspection equipment, which promotes the automation of parameter measurement. In this paper, we present a novel method based on deep learning to recognize point clouds of OCS components. The method identifies the context of each single frame point cloud by a convolutional neural network (CNN) and combines some single frame data based on classification results, then inputs them into a segmentation network to identify OCS components. To verify the method, we build a point cloud dataset of OCS components that contains eight categories. The experimental results demonstrate that the proposed method can detect OCS components with high accuracy. Our work can be applied to the real OCS components detection and has great practical significance for OCS automatic inspection.