The clock gene Bmal1 controls inflammatory mediators in rheumatoid arthritis fibroblast-like synoviocytes.

OBJECT: To clarify the involvement of clock genes in the production of inflammatory mediators from RA-FLS, we examined the role of Bmal1, one of the master clock genes. METHODS: RA-FLSs were stimulated with IL-1ß (0, 20 ng/mL), IL-6 (0, 20 ng/mL), IL-17 (0, 20 ng/mL), TNF-α (0, 20 ng/mL) or IFN-γ (0, 20 ng/mL) to examine the expression of Bmal1, MMP-3, CCL2, IL-6, IL-7 and IL-15 by qPCR and immunofluorescence staining. After silencing Bmal1, RA-FLSs were stimulated with IL-1ß (0, 20 ng/mL), TNF-α (0, 20 ng/mL) or IFN-γ (0, 20 ng/mL) to examine the expressions of inflammatory mediators; MMP-3, CCL2, IL-6 and IL-15 by qPCR, ELISA and immunofluorescence staining. RESULTS: Bmal1 expressions were increased by IL-1ß, TNF-α and IFN-γ stimulations. Under stimulations with TNF-α, IL-1ß, and IFN-γ, mRNA and protein expressions of MMP-3, CCL2 and IL-6 were suppressed by siBmal1. CONCLUSION: Results indicate that Bmal1 contributes the production of MMP-3, CCL2, and IL-6 from RA-FLS, implying Bmal1 is involved in the pathogenesis of RA by regulating the inflammation.

[Improvement of Motion Artifacts in Brain MRI Using Deep Learning by Simulation Training Data].

PURPOSE: To test whether deep learning can be used to effectively reduce artifacts in MR images of the brain. METHODS: In this study, a large set of images with and without motion artifacts is needed for training. It is difficult to collect training data from clinical images because it requires a lot of effort and time. We have created motion artifact images of the brain by computer simulation. As an experimental study, we obtained original images for deep learning from 20 volunteers. These original images were used to create various images of different artifacts by computer simulation and these were used the input images for deep learning. The same method was used to create test images and these images were used to compare the structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) between the input images and output images using the three denoising methods. The network models used were U-shaped fully convolutional network (U-Net), denoising convolutional neural network (DnCNN) and wide inference network and 5 layers Residual learning and batch normalization (Win5RB). RESULTS: U-Net was the most effective model for reducing motion artifacts. The SSIM and PSNR were 0.978 and 32.5 dB. CONCLUSION: This is an effective method to reduce artifacts without degrading the image quality of brain MRI images.

[Development of Motion Artifact Generator for Deep Learning in Brain MRI].

PURPOSE: We focused on deep learning for a reduction of motion artifacts in MRI. It is difficult to collect a large number of images with and without motion artifacts from clinical images. The purpose of this study was to create motion artifact images in MRI by simulation. METHODS: We created motion artifact images by computer simulation. First, 20 different types of vertical pixel-shifted images were created with different shifts, and the amount of pixel shift was set from -10 to 10 pixels. The same method was used to create pixel-shifted images for horizontal shift, diagonal shift, and rotational shift, and a total of 80 types of pixel-shifted images were prepared. These images were Fourier transformed to create 80 types of k-space data. Then, phase encodings in these k-space data were randomly sampled and Fourier transformed to create artifact images. The reproducibility of the simulation images was verified using the deep learning network model of U-net. In this study, the evaluation indices used were the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). RESULTS: The average SSIM and PSNR for the simulation images were 0.95 and 31.5, respectively; those for the clinical images were 0.96 and 31.1, respectively. CONCLUSION: Our simulation method enables us to create a large number of artifact images in a short time, equivalent to clinical artifact images.

Evaluation of anticancer activities of benzo[c]phenanthridine alkaloid sanguinarine in oral squamous cell carcinoma cell line.

While the effects of benzo[c]phenanthridine alkaloids (QBA), known mainly as sanguinarine and chelerythrine, on the inhibition of some kinds of cancer cell proliferation have been established, the effect on oral squamous cell is not known. Here, the antitumor activity of sanguinarine was demonstrated using in vitro assay systems in SAS, a human oral squamous cell carcinoma (OSCC) cell line. The anti-proliferative and -invasive effects were confirmed with IC50 values in the concentration range of 0.75-1.0 µM by MTT assay and invasive assay, respectively. Sanguinarine was also able to suppress cell anchorage-independent growth, whereas it did not affect the cells' adhering capabilities. Finally, sanguinarine induced apoptotic cell death by activating caspase and altering the Bcl-2/Bax ratio. Taken together, these results indicate that sanguinarine is a potential inhibitor of tumorigenesis and suggest that it may be valuable in the development of new anticancer drugs for the treatment of OSCC.

Anti-tumor activity of dehydroxymethylepoxyquinomicin against human oral squamous cell carcinoma cell lines in vitro and in vivo.

Several reports have indicated that nuclear factor-kappa B (NF-κB) is constitutively activated in a variety of cancer cells including human oral squamous carcinoma cells, and play a key role in their growth and survival. Recent studies report that NF-κB inhibitor, dehydroxymethylepoxyquinomicin (DHMEQ), inhibits proliferation and induces apoptosis in prostate cancer cell lines. However this anti-tumor effects are still unknown in end human oral squamous carcinoma cells. In the present study, we investigated the effects of DHMEQ on oral squamous carcinoma cell (OSCC) lines in vitro and in vivo. Human OSCC cell lines (HSC-3, SAS) were treated with DHMEQ and examined for cell viability by MTT assay, cell cycle distribution by flow-cytometry, apoptosis by TUNEL assay, and protein expression by western blotting, respectively. In vivo activities were also investigated in a mouse xenograft model. DHMEQ inhibited growth of two OSCC cell lines in a dose-dependent manner measured by MTT assay. A flow cytometric analysis demonstrated that treatment with DHMEQ induced accumulation in sub-G1 phase. TUNEL assay showed that DHMEQ induced DNA fragmentation. Protein expression by western blotting analysis revealed that DHMEQ induced nuclear down regulation of Survivin, cIAP-1, and cIAP-2. In nude mice, DHMEQ inhibited growth of OSCC without major toxic side effects. The present results demonstrated that administration of DHMEQ is suggested to be a novel anti-tumor approach to the treatment of OSCC.