The incidence of stomach cancer has been found to be gradually decreasing; however, it remains one of the most frequently occurring malignant cancers in Korea. According to statistics of 2017, stomach cancer is the top cancer in men and the fourth most important cancer in women, necessitating methods for its early detection and treatment. Considerable research in the field of bioinformatics has been conducted in cancer studies, and bioinformatics approaches might help develop methods and models for its early prediction. We aimed to develop a classification method based on deep learning and demonstrate its application to gene expression data obtained from patients with stomach cancer. Data of 60,483 genes from 334 patients with stomach cancer in The Cancer Genome Atlas were evaluated by principal component analysis, heatmaps, and the convolutional neural network (CNN) algorithm. We combined the RNA-seq gene expression data with clinical data, searched candidate genes, and analyzed them using the CNN deep learning algorithm. We performed learning using the sample type and vital status of patients with stomach cancer and verified the results. We obtained an accuracy of 95.96% for sample type and 50.51% for vital status. Despite overfitting owing to the limited number of patients, relatively accurate results for sample type were obtained. This approach can be used to predict the prognosis of stomach cancer, which has many types and underlying causes.

Saline-tab water (2.5 L) with 0, 2.5, 5, and 10% saline solution contaminated by P. aeroginosa or S. aureus, was electrolyzed with constant electrical current of 2A or 4A for different time durations (1, 2, 4, 8, and 16min). The electrolysis with 2A-4min showed disinfection effect against P. aeroginosa of 105 CFU/㎖ in all saline concentrations. When the electrical current was raised to 4A, P. aeroginosa of 106 CFU/㎖ was disinfected in 4 min. S. aureus of 105 CFU/㎖ was disinfected with 2A-2 min in all saline concentrations. S. aureus of 106 CFU/㎖ was completely disinfected with 2A-8 min. To compare the effect of constant current electrolysis with that of intermittent current electrolysis, solution contaminated with P. aeroginosa of 106 CFU/㎖ was electrolyzed with several pairs of intermittent current of 2A for 2 min followed by 2min pause. Disinfecting effect of intermittent electrolysis was very similar to the constant current electrolysis without pause in 16 min. The present study demonstrated that the direct electrolyzing process with no septum membrane is a convenient and economic sterilization method.