Network-Based Analysis for Uncovering Mechanisms Underlying Alzheimer's Disease

Analysis of PICC Based on Dysfunction Module Personalized Nursing Treatment in Chemotherapy of Advanced Esophageal Cancer

Esophageal cancer is a common malignant tumor in some countries and regions in the world, which threatens the health of people all over the world. Due to esophageal cancer, about 400,000 people die every year. Among them, the number of cases and deaths of esophageal cancer in our country accounts for about 50% of the world. This article aims to study the clinical efficacy of PICC catheterization in the chemotherapy of advanced esophageal cancer based on the dysfunction module. For patients with advanced esophageal cancer, traditional surgery is still difficult to achieve satisfactory results. Therefore, in order to improve the therapeutic effect of esophageal cancer and obtain a higher clinical effective rate and pathological complete remission rate, it is necessary to explore a new comprehensive treatment model. In this paper, the theoretical knowledge of PICC is described in detail from the concept, advantages, and indications of PICC, and the SPSS statistical method is proposed. The SPSS statistical model, mean test, two-independent sample t-test, and regression analysis jointly explain the application principles and methods of SPSS statistical methods. After selecting the experimental subjects, the 180 eligible patients with esophageal squamous cell carcinoma were divided into two groups, each with 30 patients with stage II esophageal cancer, 30 patients with stage III esophageal cancer, and 30 patients with stage IV esophageal cancer. Grouping according to different variables, real-time recording of data, and SPSS statistical method to analyze the experimental data, the experimental results of this article show that the nursing satisfaction of the observation group is 92.2%, which is significantly higher than the 56.7% of the control group, so it can be seen that PICC plays an important role in the personalized care and treatment of advanced esophageal cancer.

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer's Disease

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer’s disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer’s disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer’s disease brains. The biological pathways associated with Alzheimer’s disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature.