AIM:To study the effects of down-regulation of guanine nucleotide exchange protein 100 (GEP100) on the invasive ability of pancreatic cancer cell AsPC-1 in vitro.METHODS:The clone of AsPC-1 cells with stable knock-down of GEP100 by transfection of pSuper-retro-puro-GEP100 was established.The invasive ability was evaluated by matrigel invasion assay and the migratory ability of the cells was examined by crossing-river test.The protein expression of E-cadherin was determined by Western blotting.RESULTS:The invasive ability was inhibited significantly in matrigel invasion assay (P

Objective To investigate the effects of cyclooxygenase-2 (COX-2) on the expressions of vascular endothelial growth factor (VEGF) and prostaglandin E_2 (PGE_2) in pancreatic carcinoma both in vitro and in vivo, and to clarify the possible mechanism of PGE_2 in mediating COX-2 inducing angiogenesis of pancreatic carcinoma. Methods In vitro study, the inhibitory effects of Celebrex, a selective cyclooxygenase-2 inhibitor, on the expression of VEGF and PGE_2 in pancreatic carcinoma cell lines PC-3 were determined using either enzyme-linked immuno-absorbent assay (ELISA) or radioimmunoassay (RIA). Effect of exogenous PGE_2 on the down-regulation of VEGF by Celebrex was also assessed. In vivo study, PC-3 cell line xenograft nude mice model was established. Changes of VEGF expression and PGE_2 of tumor tissues after the treatment of Celebrex were investigated using Western blotting or RIA. Results Celebrex suppressed the expressions of VEGF and PGE_2 in cultured PC-3 cell line with a manner of dose- and time-dependence. Exogenous PGE_2 up-regulated the expression of VEGF, which was suppressed by Celebrex in a dose-dependent fashion. In vivo study, administration of Celebrex into xenograft nude mice inhibited expressions of VEGF and PGE_2 significantly. Conclusion COX-2 is involved in angiogenesis in pancreatic carcinoma probably through the inhibition of the production of angiogenic factors such as VEGF. PGE_2 is likely to act as an important mediator in this process.

AIM: To investigate the effects of the dominant-negative I?B? plasmid on the expression of NF-?B and cyclooxygenase-2 (COX-2) after its being transfected into pancreatic carcinoma PC-3 cell line. METHODS: The expression of NF-?B and COX-2 in PC-3 cell line was confirmed by immunohistochemistry. The effects of dominant-negative I?B? plasmid transfection on the expression of NF-?B and COX-2 were studied by reverse transcription polymerase chain reaction (RT-PCR) and Western blotting. RESULTS: Both NF-?B and COX-2 were expressed in pancreatic carcinoma PC-3 cell line, and the expression of NF-?B and COX-2 were down-regulated in a certain time-independent manner after dominant-negative I?B? plasmid transfection. CONCLUSIONS: The NF-?B and COX-2 are expressed in pancreatic carcinoma PC-3 cell lines. The expression of NF-?B and COX-2 are inhibited by dominant-negative I?B? plasmid, while NF-?B is likely to play an important role in regulating the expression of COX-2.

Objective To establish an artificial intelligence deep learning model using clinical colonoscopy images and video to assist the diagnosis by colonoscopy. Methods More than 600000 colonoscopy images were collected in endoscopic center of the Second Affiliated Hospital of Zhejiang University School of Medicine from 2014 to 2018, and endoscopic experts recorded a large number of high-quality operation video of colonoscopy as analysis data. After repeated discussion by six experts, the classified intestinal sites and pathological features were determined, and fuzzy and confusable images were deleted. The final selection result was approximately 1 out of 4. And then the features of images were marked using an independently developed software. The deep learning algorithm was developed using TensorFlow platform of Google. Results After repeated comparison and analysis of the results of machine training and judgment results combined with pathology from endoscopic experts, the sensitivity of the model for some diseases ( such as colon polyps) was 99％ under laboratory conditions. In the clinical colonoscopy test, the sensitivity, specificity, and overall accuracy of this model for diagnosis of colon polyps were 98. 30％(4187/4259), 88. 10％ (17620/20000), and 92. 92％ [2×98. 30％×88. 10％/(98. 30％+88. 10％)], respectively. The sensitivity and specificity for ulcerative colitis were 78. 32％ ( 2671/3410) , and 67. 06％(13412/20000), respectively. The diagnosis time spent on a single image was 0. 5±0. 03 s, and it was the real time for application, including system recognition, text prompt in video image, background record and storage. Conclusion The artificial intelligence assisted diagnosis model developed by our team can identify colonic polyps, colorectal cancer, colorectal eminence, colonic diverticulum, ulcerative colitis, etc. The auxiliary diagnosis model of colon disease can guide beginners to carry out colonoscopy, and can improve lesion detection rate, reduce misdiagnosis rate, and improve the overall operating efficiency of endoscopic center, which is conducive to the quality control of colonoscopy.