Bipolar Disorder and Oxidative Stress Injury Mechanism - Clinical Big Data Analysis Based on Machine Learning

Background: Exercise training has been extensively studied in heart failure (HF) and psychological disorders, which has been shown to worsen each other. However, our understanding of how exercise simultaneously protect heart and brain of HF patients is still in its infancy. The purpose of this study was to take advantage of big data techniques to explore hotspots and frontiers of mechanisms that protect the heart and brain simultaneously through exercise training.Methods: We studied the scientific publications on related research between January 1, 2003 to December 31, 2020 from the WoS Core Collection. Research hotspots were assessed through open-source software, CiteSpace, Pajek, and VOSviewer. Big data analysis and visualization were carried out using R, Cytoscape and Origin.Results: From 2003 to 2020, the study on HF, depression, and exercise simultaneously was the lowest of all research sequences (two-way ANOVAs, p < 0.0001). Its linear regression coefficient r was 0.7641. The result of hotspot analysis of related keyword-driven research showed that inflammation and stress (including oxidative stress) were the common mechanisms. Through the further analyses, we noted that inflammation, stress, oxidative stress, apoptosis, reactive oxygen species, cell death, and the mechanisms related to mitochondrial biogenesis/homeostasis, could be regarded as the primary mechanism targets to study the simultaneous intervention of exercise on the heart and brain of HF patients with depression.Conclusions: Our findings demonstrate the potential mechanism targets by which exercise interferes with both the heart and brain for HF patients with depression. We hope that they can boost the attention of other researchers and clinicians, and open up new avenues for designing more novel potential drugs to block heart-brain axis vicious circle.

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An Automated Analytics Engine for College Program Selection using Machine Learning and Big Data Analysis

10.5121/csit.2021.111417 ◽

2021 ◽

Author(s):

Jinhui Yu ◽

Xinyu Luan ◽

Yu Sun

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Web Pages ◽

Python Language ◽

Program Selection ◽

Front End

Because of the differences in the structure and content of each website, it is often difficult for international applicants to obtain the application information of each school in time. They need to spend a lot of time manually collecting and sorting information. Especially when the information of the school may be constantly updated, the information may become very inaccurate for international applicants. we designed a tool including three main steps to solve the problem: crawling links, processing web pages, and building my pages. In compiling languages, we mainly use Python and store the crawled data in JSON format [4]. In the process of crawling links, we mainly used beautiful soup to parse HTML and designed crawler. In this paper, we use Python language to design a system. First, we use the crawler method to fetch all the links related to the admission information on the school's official website. Then we traverse these links, and use the noise_remove [5] method to process their corresponding page contents, so as to further narrow the scope of effective information and save these processed contents in the JSON files. Finally, we use the Flask framework to integrate these contents into my front-end page conveniently and efficiently, so that it has the complete function of integrating and displaying information.

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Lysophosphatidylcholine Offsets the Protective Effects of Bone Marrow Mesenchymal Stem Cells on Inflammatory Response and Oxidative Stress Injury of Retinal Endothelial Cells via TLR4/NF-κB Signaling

Journal of Immunology Research ◽

10.1155/2021/2389029 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Haijun Zhao ◽

Yanhui He

Keyword(s):

Oxidative Stress ◽

Stem Cells ◽

Bone Marrow ◽

Endothelial Cells ◽

Inflammatory Response ◽

Protective Effects ◽

Stress Injury ◽

Oxidative Stress Injury ◽

Marrow Mesenchymal Stem Cells ◽

And Oxidative Stress

Diabetic retinopathy (DR), as a major cause of blindness worldwide, is one common complication of diabetes mellitus. Inflammatory response and oxidative stress injury of endothelial cells play significant roles in the pathogenesis of DR. The study is aimed at investigating the effects of lysophosphatidylcholine (LPC) on the dysfunction of high glucose- (HG-) treated human retinal microvascular endothelial cells (HRMECs) after being cocultured with bone marrow mesenchymal stem cells (BMSCs) and the underlying regulatory mechanism. Coculture of BMSCs and HRMECs was performed in transwell chambers. The activities of antioxidant-related enzymes and molecules of oxidative stress injury and the contents of inflammatory cytokines were measured by ELISA. Flow cytometry analyzed the apoptosis of treated HRMECs. HRMECs were further treated with 10-50 μg/ml LPC to investigate the effect of LPC on the dysfunction of HRMECs. Western blotting was conducted to evaluate levels of TLR4 and p-NF-κB proteins. We found that BMSCs alleviated HG-induced inflammatory response and oxidative stress injury of HRMECs. Importantly, LPC offsets the protective effects of BMSCs on inflammatory response and oxidative stress injury of HRMECs. Furthermore, LPC upregulated the protein levels of TLR4 and p-NF-κB, activating the TLR4/NF-κB signaling pathway. Overall, our study demonstrated that LPC offsets the protective effects of BMSCs on inflammatory response and oxidative stress injury of HRMECs via TLR4/NF-κB signaling.

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