[ ABSTRACT] AIM:To investigate the inhibitory effect of ginsenoside Re on intimal hyperplasia induced by bal-loon-injury and to explore the role of NF-κB p65 signaling pathway in the process.METHODS:SD rats (n=40) were di-vided into 5 groups randomly: sham operation group, model group, low-dose ginsenoside Re group, middle-dose ginsen-oside Re group and high-dose ginsenoside Re group.The carotid artery intima injury model was established by 2F balloon catheters in all groups except the sham operation group.The day after modeling, the animals in model group and sham op-eration group were administered intragastrically with distilled water, and the rats in low-dose, middle-dose and high-dose ginsenoside Re groups were given ginsenoside Re at doses of 12.5 mg/kg, 25mg/kg and 50 mg/kg, respectively.After 14 continuous days, the morphological changes of the injured arteries were observed by HE staining and the lumen area, intima area and media area as well as the ratio of intimal area/media area were determined.The expression of tumor necrosis fac-tor-α( TNF-α) and interleukin-1β( IL-1β) were detected by real-time PCR.The proliferating cell nuclear antigen ( PC-NA) and nuclear factor-kappa B ( NF-κB) p65 were examined by immunohistochemistry.RESULTS:Compared with sham operation group, the vessel cavity was narrowed (P<0.01), the mRNA levels of TNF-αand IL-1β, and the protein ex-pression of PCNA and NF-κB p65 were increased in model group (P<0.05).Compared with model group, the vascular intimal hyperplasia was alleviated obviously (P<0.05), and the mRNA levels of TNF-αand IL-1β, and protein expres-
sion of PCNA and NF-κB p65 were decreased in medium and high-dose ginsenoside Re groups (P<0.05).CONCLU-SION:Ginsenoside Re inhibits the vascular neointimal hyperplasia induced by balloon-injury in rats, and the molecular mechanism may be related to the inhibition of NF-κB p65 signaling pathway.

Objective:To validate the feasibility of a deep learning-based clinical target volume (CTV) auto-segmentation algorithm for cervical cancer in clinical settings.Methods:CT data sets from 535 cervical cancer patients were collected. CTVs were delineated according to RTOG and JCOG guidelines, reviewed by experts, and then used as reference contours for training (definitive 177, post-operative 302) and test (definitive 23, post-operative 33). Four definitive and 6 post-operative cases were randomly selected from the testing cohort to be manually delineated by junior, intermediate, senior doctors, respectively. Dice coefficient (DSC), mean surface distance (MSD) and Hausdorff distance (HD) were used for test and comparison between auto-segmentation and RO delineation. Meantime, auto-segmentation time and manual delineation time were recorded.Results:Auto-segmentation models of dCTV 1, dCTV 2 and pCTV 1 were trained with VB-Net and showed good agreement with reference contours in the testing cohorts (DSC, 0.88, 0.70, 0.86 mm; MSD, 1.32, 2.42, 1.15 mm; HD, 21.6, 22.4, 20.8 mm). For dCTV 1, the difference between auto-segmentation and all three groups of doctors was not significant ( P>0.05). For dCTV 2 and pCTV 1, auto-segmentation was better than the junior and intermediate doctors (both P<0.05). Auto-segmentation time consumption was considerably shorter than that of manual delineation. Conclusions:Deep learning-based CTV auto-segmentation algorithm for cervical cancer achieves comparable accuracy to manual delineation of senior doctors. Clinical application of the algorithm can contribute to shortening doctors′ manual delineation time and improving clinical efficiency. Furthermore, it may serve as a guide for junior doctors to improve the consistency and accuracy of cervical cancer CTV delineation in clinical practice.