[Meta-analysis on association between genetic polymorphisms of cytochrome P450 2E1 and susceptibility to Chinese gastric cancer].

OBJECTIVE: To conduct a comprehensive quantitative analysis about the association between genetic polymorphisms of CYP 2E1 and susceptibility to Chinese gastric cancer in order to offer evidence-based evidence for the etiology of gastric cancer. METHODS: Using the keywords and entry terms of cytochrom P450 2E1, cytochrome P450, CYP 2E1, stomach neoplasms, individual susceptibility, gene polymorphism, risk factors in English and Chinese, we searched medical literature databases, such as Pub Med, Embase, CBM, VIP, CNKI, China Info, published from January 1 th, 1997 to December 31 th, 2016, the Chines population was selected as the research object. Metaanalysis was performed using Stata 14. 0 in literature that selected quality studies of original literatures of more than 6 stars. RESULTS: There were 10 high-quality original articles covering high, middle and low incidence areas of gastric cancer in our country, with 832 cases and 1018 controls were included, which fit the HWE test. The population with CYP 2E1 C1C2 genotype have a lower risk of developing gastric cancer than the population with CYP 2E1 C1C1 genotype( OR = 0. 650, P < 0. 001, 95% CI 0. 515-0. 821); Subgroup-analysis result reveals that, in smaller sample size population with CYP 2E1 C1C1 genotype have a gastric cancer risk of 2. 02 times the risk of having gastric cancer with CYP 2E1 C1C2 and C2C2 genotypes( P < 0. 001, 95% CI 1. 55-2. 64), in bigger sample size population with CYP 2E1 C1C1 genotype have a gastric cancer risk of0. 93 times the risk of having gastric cancer with CYP 2E1 C1C2 and C2C2 genotypes( P = 0. 586, 95% CI 0. 71-1. 22), in overall sample size population with CYP 2E1 C1C1 genotype have a gastric cancer risk of 1. 50 times the risk of having gastric cancer with CYP 2E1 C1C2 and C2C2 genotypes( P = 0. 006, 95% CI 1. 12-2. 00). CONCLUSION: The size of the sample is an important factor affecting the result. The small sample size of the study tends to get positive result. Whether CYP 2E1 C1C1 genotype of the population is a risk factor for gastric cancer remains to be further studied.

[Visualization analysis of literature information in Journal of Hygiene Research].

OBJECTIVE: To grasp research status and the revolution of Journal of Hygiene Research since started publishing, and also track research hot spots and developing trends of this field. METHODS: Using the method of bibliometrics and information visualization software CiteSpace III, quantities of published literature, supported funds, institutions, authors and keywords from 6775 articles published in Journal of Hygiene Research from 1972 to 2014 were analyzed. RESULTS: Amount of literatures published on Journal of Hygiene Research increased wave upon wave, the peak appeared in 1995. Institutions of Chinese Center for Disease Control and Prevention, such as National Institute for Nutrition and Health, Institute of Environmental Health and Related Product Safety, National Institute of Occupational Health and Poison Control and some medical colleges were the most productive. The scholars with the most number of publications were YANG Xiaoguang, YIN Shian and PIAO Jianhua, the researcher of National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, with more than 75 articles were published. The research contents included influencing factors, related concepts, diseases, methods and objects. "mutagenicity", "apoptosis", "lead poisoning", "HPLC" and "rat" were research focuses in this field. CONCLUSION: There were lots of matter and cooperation in the articles published on Journal of Hygiene Research. The centers for disease control and prevention in different regions and universities pay attention to the coorperation, research teams with members of various ages collaborate and grow together, form a close, complex collaborative network among authors, which promote the development of the magazine and research fields together.

Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration.

Severe Fever with thrombocytopenia syndrome (SFTS) is a highly fatal viral infectious disease that poses a significant threat to public health. Currently, the phase and pathogenesis of SFTS are not well understood, and there are no specific vaccines or effective treatment available. Therefore, it is crucial to identify biomarkers for diagnosing acute SFTS, which has a high mortality rate. In this study, we conducted differentially expressed genes (DEGs) analysis and WGCNA module analysis on the GSE144358 dataset, comparing the acute phase of SFTSV-infected patients with healthy individuals. Through the LASSO-Cox and random forest algorithms, a total of 2128 genes were analyzed, leading to the identification of four genes: ADIPOR1, CENPO, E2F2, and H2AC17. The GSEA analysis of these four genes demonstrated a significant correlation with immune cell function and cell cycle, aligning with the functional enrichment findings of DEGs. Furthermore, we also utilized CIBERSORT to analyze the immune cell infiltration and its correlation with characteristic genes. The results indicate that the combination of ADIPOR1, CENPO, E2F2, and H2AC17 genes has the potential as characteristic genes for diagnosing and studying the acute phase of SFTS virus (SFTSV) infection.

Time-Course Transcriptome Analysis Reveals Distinct Phases and Identifies Two Key Genes during Severe Fever with Thrombocytopenia Syndrome Virus Infection in PMA-Induced THP-1 Cells.

In recent years, there have been significant advancements in the research of Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV). However, several limitations and challenges still exist. For instance, researchers face constraints regarding experimental conditions and the feasibility of sample acquisition for studying SFTSV. To enhance the quality and comprehensiveness of SFTSV research, we opted to employ PMA-induced THP-1 cells as a model for SFTSV infection. Multiple time points of SFTSV infection were designed to capture the dynamic nature of the virus-host interaction. Through a comprehensive analysis utilizing various bioinformatics approaches, including diverse clustering methods, MUfzz analysis, and LASSO/Cox machine learning, we performed dynamic analysis and identified key genes associated with SFTSV infection at the host cell transcriptomic level. Notably, successful clustering was achieved for samples infected at different time points, leading to the identification of two important genes, PHGDH and NLRP12. And these findings may provide valuable insights into the pathogenesis of SFTSV and contribute to our understanding of host-virus interactions.