Rheumatoid arthritis (RA) is characterized by a pre-vascular seriously inflammatory phase, followed by a vascular phase with high increase in vessel growth. Since angiogenesis has been considered as an essential event in perpetuating inflammatory and immune responses, as well as supporting pannus growth and development of RA, inhibition of angiogenesis has been proposed as a novel therapeutic strategy for RA. Triptolide, a diterpenoid triepoxide from Tripterygium wilfordii Hook F, has been extensively used in treatment of RA patients. It also acts as a small molecule inhibitor of tumor angiogenesis in several cancer types. However, it is unclear whether triptolide possesses an anti-angiogenic effect in RA. To address this problem, we constructed collagen-induced arthritis (CIA) model using DA rats by the injection of bovine type II collagen. Then, CIA rats were treated with triptolide (11–45 µg/kg/day) starting on the day 1 after first immunization. The arthritis scores (P<0.05) and the arthritis incidence (P<0.05) of inflamed joints were both significantly decreased in triptolide-treated CIA rats compared to vehicle CIA rats. More interestingly, doses of 11∼45 µg/kg triptolide could markedly reduce the capillaries, small, medium and large vessel density in synovial membrane tissues of inflamed joints (all P<0.05). Moreover, triptolide inhibited matrigel-induced cell adhesion of HFLS–RA and HUVEC. It also disrupted tube formation of HUVEC on matrigel and suppressed the VEGF-induced chemotactic migration of HFLS–RA and HUVEC, respectively. Furthermore, triptolide significantly reduced the expression of angiogenic activators including TNF-α, IL-17, VEGF, VEGFR, Ang-1, Ang-2 and Tie2, as well as suppressed the IL1-β-induced phosphorylated of ERK, p38 and JNK at protein levels. In conclusion, our data suggest for the first time that triptolide may possess anti-angiogenic effect in RA both in vivo and in vitro assay systems by downregulating the angiogenic activators and inhibiting the activation of mitogen-activated protein kinase downstream signal pathway.

Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the CycleGAN, Pix2pix and U-Net models were used to generate the sCT images. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were used to quantify the accuracy of the proposed models in a testing cohort of 34 patients. Radiation dose were calculated on pCT and sCT following the same protocol. Dose distributions were evaluated for 4 patients by comparing the dose-volume-histogram (DVH) and 2D gamma index analysis. Results: The average MAE and RMSE values between sCT by three models and pCT reduced by 15.4 HU and 26.8 HU at least, while the mean PSNR and SSIM metrics between sCT by different models and pCT added by 10.6 and 0.05 at most, respectively. There were only slight differences for DVH of selected contours between different plans. The passing rates of 2D gamma index analysis under 3 mm/3% 3 mm/2%, 2 mm/3%and 2 mm/2% criteria were all higher than 95%. Conclusions: All the sCT had achieved better evaluation metrics than those of original CBCT, while the performance of CycleGAN model was proved to be best among three methods. The dosimetric agreement confirmed the HU accuracy and consistent anatomical structures of sCT by deep learning methods.

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