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There is lots of evidence to support the critical involvement of mTOR signaling in the carcinogenesis of hepatocellular carcinoma (HCC). However, it has not been determined how the roles of individual mTORC1 and mTORC2 inhibitors played in the HCC therapeutics. We thus compared the effects of everolimus, Ku0063794, and a combination of the two therapies on HCC cells, using various in vitro studies (HepG2, Hep3B, and Huh7 cells), ex vivo culturing of HCC tissues obtained from patients, and the in vivo mouse xenograft model of HCC cells. Our in vitro, ex vivo, and in vivo experiments consistently demonstrated that everolimus and Ku0063794 combination therapy was superior to individual monotherapies, as manifested by higher reduction of proliferation, migration, and invasion of HCC cells, and the higher inhibition of EMT process as well. Although individual monotherapies could not inhibit SIRT1 (positive regulator of EMT) expression, the combination therapy significantly inhibited SIRT1 expression. However, overexpression of SIRT1 mitigated the EMT-inhibiting effect of the combination therapy, suggesting that the combination therapy inhibits the EMT by way of suppressing SIRT1 expression. Therefore, when considering everolimus as an anti-HCC agent, the improved anticancer effects provided by combining it with an inhibitor of both mTORC1 and mTORC2 should be recognized.

Angiosarcoma is a rare tumor that account for less than 1% of all sarcomas. Although hepatic angiosarcoma usually presents with unspecific symptoms, it rapidly progresses and has a high mortality. We report a rare case of primary hepatic angiosarcoma manifested as recurrent hemoperitoneum.

Recently, generative adversarial network-based image super resolution has been investigated, and it has been shown to lead to overwhelming improvements in subjective quality. However, it also leads to checkerboard artifacts and the unpleasing high-frequency (HF) components. In this paper, we propose a multi-discriminators-based image super resolution method that distinguishes those artifacts from various perspectives. First, the DCT perspective discriminator is proposed because the checkerboard artifacts are easily separated on the frequency domain. Second, the gradient perspective discriminator is proposed, because the unpleasing HF components can be discriminated on the gradient magnitude distribution. These proposed multi-perspective discriminators can easily identify artifacts, and they can help the generator reproduce artifact-less SR images. The experimental results show that the proposed SR-GAN with multiperspective discriminators achieves objective and subjective quality improvements in terms of PSNR, SSIM, PI and MOS, as compared to the conventional SR-GAN by reducing the aforementioned artifacts. INDEX TERMS Image super-resolution, deep learning for super resolution, SR GAN, multi-discriminators.