The utility of web mining for epidemiological research: studying the association between parity and cancer risk

Web Crawling and mRNA Sequencing Analyze Mechanisms of Photobiomodulation

Background: Photobiomodulation (PBM) is praised as a promising physical therapy, which has many advantages, such as being noninvasive and painless. However, the mechanisms are not fully elucidated. Methods: Using web crawling, mRNA sequence, and bioinformatics analysis, we selected genes, functional annotation, and mechanisms. The expressions of inflammatory cytokines were measured using quantitative real-time PCR (RT-qPCR). Results: A total of 146 human genes and 57 pathways were identified about PBM. The 630 nm light-emitting diode (LED)-stimulated-MH7A cells were sequenced to further analyze the mechanism of PBM. Two thousand nine hundred fifty differentially expressed genes were identified, and the gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. The 12 pathways were matched with the KEGG results of PBM and MH7A cells. A protein-protein interaction network was performed among genes in 12 pathways, and 10 outstanding proteins were identified. Importantly, the 9 genes were predicted with potential research value. And we also demonstrated that expression of inflammatory factors [interleukin (IL)-6, IL-1β, IL-8, and matrix metalloproteinase-3 (MMP-3)] was reduced; meanwhile, the expression of anti-inflammatory factor IL-10 was promoted after 630 nm LED. Conclusions: Using web crawling, bioinformatics analysis, and mRNA sequence, we obtained 9 key genes and 12 important pathways about PBM. Importantly, we demonstrated the anti-inflammatory effect of 630 nm LED red light by RT-qPCR.

Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies

Objectives

We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method.

Methods

We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies.

Results

We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses.

Conclusions

Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

A novel web informatics approach for automated surveillance of cancer mortality trends

Cancer surveillance data are collected every year in the United States via the National Program of Cancer Registries (NPCR) and the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute (NCI). General trends are closely monitored to measure the nation's progress against cancer. The objective of this study was to apply a novel web informatics approach for enabling fully automated monitoring of cancer mortality trends. The approach involves automated collection and text mining of online obituaries to derive the age distribution, geospatial, and temporal trends of cancer deaths in the US. Using breast and lung cancer as examples, we mined 23,850 cancer-related and 413,024 general online obituaries spanning the timeframe 2008-2012. There was high correlation between the web-derived mortality trends and the official surveillance statistics reported by NCI with respect to the age distribution (ρ=0.981 for breast; ρ=0.994 for lung), the geospatial distribution (ρ=0.939 for breast; ρ=0.881 for lung), and the annual rates of cancer deaths (ρ=0.661 for breast; ρ=0.839 for lung). Additional experiments investigated the effect of sample size on the consistency of the web-based findings. Overall, our study findings support web informatics as a promising, cost-effective way to dynamically monitor spatiotemporal cancer mortality trends.