Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.

Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Background: The overall incidence and mortality of gastric cancer have steadily declined in the United States over the past few decades, but it is still a serious disease burden for patients. Therefore, it is of great significance to evaluate the latest survival rate of gastric cancer. Methods: Based on the Surveillance, Epidemiology, and End Results database, this study analyzed the age-standardized relative survival rates and survival trends of gastric cancer cases in 2007–2011 and 2012–2016 using period analysis, and the survival rate 2017–2021 was predicted using a generalized linear model based on the period analysis. Results: During 2007–2016, the 5-year relative survival rate of patients with gastric cancer continued to rise, and the same trend was observed in 2017–2021. The 5-year overall age-standardized relative survival rates in 2007–2011, 2012–2016, and 2017–2021 were 38.3%, 40.6%, and 42.9%, respectively. However, despite these favorable trends, the overall relative survival of patients with gastric cancer remains at a low level. There were significant differences in the relative survival rates of patients with gastric cancer in terms of age, sex, race, primary site, stage, and socioeconomic status. Notably, the survival rate of patients with distant-stage gastric cancer remains very low (10%). Conclusion: We found that the survival rate of patients with gastric cancer showed different degrees of improvement in each subgroup. However, the overall relative survival rate of patients with gastric cancer remains low. Analyzing the changes of patients with gastric cancer in the last 10 years will be helpful in predicting the changing trend of cancer in the future. It also provides a scientific basis for relevant departments to formulate effective tumor prevention and control measures.

Abstract:Background: The aim of this study was to explore the mechanism by which amentoflavone (AME) improves insulin resistance in a human hepatocellular liver carcinoma cell line (HepG2). Methods: A model of insulin resistant cells was established in HepG2 by treatment with high glucose and insulin. The glucose oxidase method was used to detect the glucose consumption in each group. To determine the mechanism by which AME improves insulin resistance in HepG2 cells, enzyme-linked immunosorbent assay (ELISA) and western blotting were used to detect the expression of phosphatidyl inositol 3-kinase (PI3K), Akt, and pAkt; the activity of the enzymes involved in glucose metabolism; and the levels of inflammatory cytokines. Results: Insulin resistance was successfully induced in HepG2 cells. After treatment with AME, the glucose consumption increased significantly in HepG2 cells compared with the model group (MG). The expression of PI3K, Akt, and pAkt and the activity of 6-phosphofructokinas (PFK-1), glucokinase (GCK), and pyruvate kinase (PK) increased, while the activity of glycogen synthase kinase-3 (GSK-3), phosphoenolpyruvate carboxylase kinase (PEPCK), and glucose-6-phosphatase (G-6-Pase) as well as the levels of interleukin-6 (IL-6), interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α), and C reactive protein (CRP) decreased. Conclusions: The mechanism by which treatment with AME improves insulin resistance in HepG2 cells may involve the PI3K-Akt signaling pathway, the processes of glucose oxygenolysis, glycogen synthesis, gluconeogenesis and inflammatory cytokine expression.

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