In China, the incidence of CP is rising rapidly; alcohol and biliary stones are the main causes. Endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography are highly sensitive and specific diagnostic methods.

BackgroundProtease-activated receptors (PARs) are a family of four G protein-coupled receptors expressed widely in many types of cells. PAR1, 2, and 4 have been shown to play an important role in many of the physiological activities of cells and many types of cancer cells. Esophageal carcinoma has become the fourth most common clinically diagnosed cancer and one of the top three leading causes of cancer-related deaths in China. The functions and expression patterns of PAR1, 2, and 4 in esophageal carcinoma have not published previously.MethodsHere, we systematically studied the expression of PAR1, 2, and 4 in clinical esophageal carcinoma patients and determined their role in esophageal carcinoma in vivo and in vitro through the overexpression or knockdown of PAR1, 2, and 4.ResultsWe found that the expression of PAR1 and 2 expressed higher in esophageal carcinoma than in the paracarcinoma tissues on clinical patients. PAR1 and 2 enhanced cell proliferation both in vivo and in vitro and reduced apoptosis to strengthen cancer cell vitality in TE-1 cells. In contrast, the expression of PAR4 expressed decreased in esophageal carcinoma, and its expression induced apoptosis in vivo and vitro.ConclusionIn our previous studies and the present study, we noted that the expression of PAR1, 2, and 4 was almost absent in different stages of esophageal carcinoma. PAR1 and 2 might be potential molecular markers for esophageal carcinoma, and PAR4 might be an effective treatment target for esophageal carcinoma prevention and treatment.

Since communication network is introduced into control system, induced-delay appears. Because of the delay, the performance of networked control system becomes bad, even unsteady. Conventional Smith predictor is sensitive to error in object model and needs delay’s value in advance. Regarding random delay, its application is limited. In this paper, we propose a method based on induced-delay predicted by BP neural network, which use two historical delay values to predict the next one. Smith predictor adjusts its parameters according to that value in time. The simulating results indicate that the precision of delay-predicting can be ensured and the performance of networked control system has been improved.