10.3969/j.issn.1000-8179.2013.12.001

Objective:To investigate the expressions of miRNA-4686 (miR-4686) and protein disulfide isomerase A4 (PDIA4) in esophageal cancer tissues, and the effect of miR-4686 on the proliferation and migration of esophageal cancer cells in vitro and possible mechanisms.Methods:The complementary integration of miR-4686 and PDIA4 mRNA were predicted using the miRWalk version 3 online website. The expression profiles of miR-4686 and PDIA4 mRNA were obtained from the University of California, Santa Cruz Genome (UCSC) database, and the relative expression of miR-4686 and PDIA4 mRNA in esophageal cancer tissues and paracancerous tissues were analyzed. The UCSC database was used to obtain the localization of miR-4686 in human cells. The relative expressions of miR-4686 and PDIA4 mRNA in esophageal cancer Eca109, TE-13, EC9706 and KYSE-510 cells and normal esophageal epithelial HET-1A cells were detected by real-time fluorescent quantitative polymerase chain reaction (qRT-PCR). Eca109 cells with the lowest relative expression of miR-4686 were selected for subsequent research, and the Eca109 cells were divided into miR-4686 group (transfected with miR-4686 mimic) and miR-NC group (transfected with miR-4686 negative control sequence mimic). The proliferation ability of Eca109 cells in the two groups was detected by colony formation assay, the migration ability of Eca109 cells was detected by scratch healing assay, and the targeting relationship between miR-4686 and PDIA4 mRNA was verified by dual luciferase reporter gene assay; the expressions of PDIA4 protein and PI3K-AKT-mTOR signaling pathway-related proteins were detected by Western blotting.Results:Compared with the paracancerous tissues, miR-4686 expression was low and PDIA4 mRNA expression was high in esophageal cancer tissues (both P < 0.01). Compared with normal esophageal epithelial HET-1A cells (0.98±0.15), the relative expression of miR-4686 in esophageal cancer Eca109 (0.11±0.04), TE-13 (0.58±0.10), EC9706 (0.34±0.05) and KYSE-510 cells (0.69±0.06) were all decreased (all P < 0.05). Compared with the miR-NC group, the relative expression of miR-4686 in Eca109 cells of the miR-4686 group increased (9.4±2.1 vs. 1.0±0.4, t = 3.88, P = 0.008), the number of colony formation decreased (38±9 vs. 114±18, t = 3.78, P = 0.009), the scratch healing rate decreased [(27.13±0.91)% vs. (45.05±3.89)%, t = 4.48, P = 0.004], and the relative expression of PDIA4 mRNA decreased [1.0±0.5 vs. 6.3±0.9, t = 5.04, P = 0.002]. Compared with wild type (WT)-PDIA4+miR-NC group, the relative luciferase activity of Eca109 cells in WT-PDIA4+ miR-4686 group decreased (0.31±0.08 vs. 0.99±0.08, t = 5.96, P < 0.001). Compared with miR-NC group, the expressions of PDIA4, p-PI3K, p-AKT and p-m-TOR proteins in Eca109 cells of the miR-4686 group were all decreased. Conclusions:In esophageal cancer tissues, miR-4686 is lowly expressed and PDIA4 mRNA is highly expressed. miR-4686 may inhibit the proliferation and migration of esophageal cancer Eca109 cells by targeting regulation of PDIA4 expression.

Objective To investigate the application of artificial intelligence-based follow-up plan for patients after percutaneous coronary intervention(PCI).Methods The follow-up plan of artificial intelligence after PCI was constructed through literature review and expert correspondence consultation.A total of 82 patients with coronary heart disease who underwent PCI in the Second Affiliated Hospital of Nanchang University from October 2022 to November 2023 were selected and divided into intervention group and control group according to random number table method,with 41 cases in each group.The control group was given routine nursing program,and the intervention group was added artificial intelligence-based follow-up program on the basis of control group.The incidence of major adverse cardiovascular event(MACE)within 6 months,30 days readmission rate,medication compliance and satisfaction score were compared between two groups.Results The incidence of MACE within 6 months and 30 days readmission rate in intervention group were significantly lower than those in control group(P＜0.05).After 6 months of intervention,Morisky scale score and satisfaction score of patients in two groups were significantly higher than before intervention(P＜0.05),and Morisky scale score and satisfaction score of patients in intervention group were significantly higher than those in control group(P＜0.05).Conclusion Follow-up program based on artificial intelligence technology can reduce the incidence of MACE and 30 days readmission rate of patients after PCI.