Interferon alpha regulates MAPK and STAT1 pathways in human hepatoma cells

<p>Abstract</p> <p>Background</p> <p>Signaling events triggered by interferon (IFN) account for the molecular mechanisms of antiviral effect. JAK-STAT pathway plays a critical role in IFN signaling, and other pathways are also implicated in IFN-mediated antiviral effect. Changes in mitogen-activated protein kinase (MAPK) and STAT1 pathways were evaluated in human hepatoma cells Huh7 and HepG2 upon IFN alpha treatment.</p> <p>Results</p> <p>Phosphorylation of ERK was significantly and specifically up-regulated, whereas enhanced phosphorylation of upstream kinase MEK was unobservable upon IFN alpha treatment. A mild increase in p38 MAPK, SAPK/JNK and downstream target ATF-2 phosphorylation was detectable after exposure to IFN alpha, indicating differential up-regulation of the MAPK signaling cascades. Moreover, STAT1 phosphorylation was strongly enhanced by IFN alpha.</p> <p>Conclusion</p> <p>IFN alpha up-regulates MAPK and STAT1 pathways in human hepatoma cells, and may provide useful information for understanding the IFN signaling.</p

Periodontal Disease Is Associated With Increased Risk of Hypertension: A Cross-Sectional Study

Objective: Published evidence showed that periodontal disease is associated with hypertension. However, relevant findings remain controversial, with few evidences focusing on Chinese population. Therefore, the aim of this study was to investigate the association between periodontal disease and hypertension in Chinese population.Methods: A total of 4,930 participants from an available health examination that was carried out in 2017 were selected for this retrospective study. The correlations between periodontal disease and hypertension were investigated using univariate and multiple logistic regression analyses and propensity score adjusted analysis. Interaction and subgroup analyses were also used to detect variable factors.Results: Finally, a total of 3,952 participants aged 30–68 years were eligible for this study. The results showed that hypertension risk was statistically significant associated with periodontal disease either in unadjusted (OR = 1.28, 95%CI = 1.14–1.47) or in adjusted (OR = 1.34, 95%CI = 1.14–1.58) model. Result from propensity score adjusted analysis also demonstrated a similar association (OR = 1.23, 95%CI = 1.06–1.42).Conclusion: Periodontal disease is significantly and positively correlated with increased risk of hypertension in Chinese population, and exact mechanisms of this association should be explored in future

Identification of Leptospira interrogans phospholipase C as a novel virulence factor responsible for intracellular free calcium ion elevation during macrophage death

Background: Leptospira-induced macrophage death has been confirmed to play a crucial role in pathogenesis of leptospirosis, a worldwide zoonotic infectious disease. Intracellular free Ca2+ concentration ([Ca2+]i) elevation induced by infection can cause cell death, but [Ca2+]i changes and high [Ca2+]i-induced death of macrophages due to infection of Leptospira have not been previously reported. Methodology/Principal Findings: We first used a Ca2+-specific fluorescence probe to confirm that the infection of L. interrogans strain Lai triggered a significant increase of [Ca2+]i in mouse J774A.1 or human THP-1 macrophages. Laser confocal microscopic examination showed that the [Ca2+]i elevation was caused by both extracellular Ca2+ influx through the purinergic receptor, P2X7, and Ca2+ release from the endoplasmic reticulum, as seen by suppression of [Ca2+]i elevation when receptor-gated calcium channels were blocked or P2X7 was depleted. The LB361 gene product of the spirochete exhibited phosphatidylinositol phospholipase C (L-PI-PLC) activity to hydrolyze phosphatidylinositol-4,5-bisphosphate (PIP2) into inositol-1,4,5-trisphosphate (IP3), which in turn induces intracellular Ca2+ release from endoplasmic reticulum, with the Km of 199 µM and Kcat of 8.566E-5 S-1. Secretion of L-PI-PLC from the spirochete into supernatants of leptospire-macrophage co-cultures and cytosol of infected macrophages was also observed by Western Blot assay. Lower [Ca2+]i elevation was induced by infection with a LB361-deficient leptospiral mutant, whereas transfection of the LB361 gene caused a mild increase in [Ca2+]i. Moreover, PI-PLCs (PI-PLC-β3 and PI-PLC-γ1) of the two macrophages were activated by phosphorylation during infection. Flow cytometric detection demonstrated that high [Ca2+]i increases induced apoptosis and necrosis of macrophages, while mild [Ca2+]i elevation only caused apoptosis. Conclusions/Significance: This study demonstrated that L. interrogans infection induced [Ca2+]i elevation through extracellular Ca2+ influx and intracellular Ca2+ release cause macrophage apoptosis and necrosis, and the LB361 gene product was shown to be a novel PI-PLC of L. interrogans responsible for the [Ca2+]i elevation

ST-V-Net: Incorporating Shape Prior Into Convolutional Neural Netwoks For Proximal Femur Segmentation

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance

A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images

Purpose: Proximal femur image analyses based on quantitative computed tomography (QCT) provide a method to quantify the bone density and evaluate osteoporosis and risk of fracture. We aim to develop a deep-learning-based method for automatic proximal femur segmentation. Methods and Materials: We developed a 3D image segmentation method based on V-Net, an end-to-end fully convolutional neural network (CNN), to extract the proximal femur QCT images automatically. The proposed V-net methodology adopts a compound loss function, which includes a Dice loss and a L2 regularizer. We performed experiments to evaluate the effectiveness of the proposed segmentation method. In the experiments, a QCT dataset which included 397 QCT subjects was used. For the QCT image of each subject, the ground truth for the proximal femur was delineated by a well-trained scientist. During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models; the rest of the subjects were used to evaluate the performance of models. Results: Visual comparison demonstrated high agreement between the model prediction and ground truth contours of the proximal femur portion of the QCT images. In the entire cohort, the proposed model achieved a Dice score of 0.9815, a sensitivity of 0.9852 and a specificity of 0.9992. In addition, an R2 score of 0.9956 (p<0.001) was obtained when comparing the volumes measured by our model prediction with the ground truth. Conclusion: This method shows a great promise for clinical application to QCT and QCT-based finite element analysis of the proximal femur for evaluating osteoporosis and hip fracture risk

Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength

The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Method: We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. With an analytical solution of the product of Gaussian distribution, we adopted variational inference to train the designed MVAE-PoE model to perform common latent feature extraction. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. Results: The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Compared to existing multi-view information fusion methods, the proposed MVAE-PoE achieved the best performance. Conclusion: The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation dosage and clinical costs from QCT.Comment: 16 pages, 3 figure