Background New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19. Methods We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman’s correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. Results Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10− 6; cough: rs=0.592, p=4.485× 10− 4; fatigue: rs=0.629, p=1.494× 10− 4; sputum production: rs=0.648, p=8.206× 10− 5; shortness of breath: rs=0.656, p=6.182× 10–5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value’s optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever. Conclusion The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public’s attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

Objective: The COL4A family genes (COL4As) are a set of extracellular matrix-related genes that have been proved a tight relationship among various cancers. However, the functional role of different COL4As (COL4A1/2/3/4/5/6) in clear cell renal cell carcinoma (ccRCC) is unclear. Methods: We obtained the data from online open-access databases including ONCOMINE, UALCAN, GEPIA, Cancer Genome Atlas (TCGA), cBioPortal, METASCAPE, STRING, TIMER, GSCALite, MEXPRESS, and TISIDB to explore the correlation between COL4As expression and genome-wide difference, progression, prognosis, genetic mutation, functional enrichment, tumor immune microenvironment, and methylation in ccRCC patients. Results: The significantly higher COL4A1/2 expression and lower COL4A3/4/5/6 expression were observed in ccRCC tissues than in normal kidney tissues. Transcriptomic levels of COL4A1/2/3/4 were significantly correlated with tumor grade and stage. The higher expression levels of COL4A1/2/3/4 were accompanied by a longer overall survival time (OS); the higher expression levels of COL4A3/4 with lower expression levels of COL4A5 were associated with a longer disease-free time (DFS). Univariate/multivariate regression model analysis showed that COL4A4 could be a potential independent biomarker for ccRCC prognosis. And a high mutation rate (29%) of COL4As was observed in ccRCC patients. However, there were no relationships between mutation rates of COL4As and OS, DFS in ccRCC patients (p>0.05). Besides, we founded that the COL4As expressions were significant associated with the infiltration of the immune cells, tumor-infiltrating lymphocytes, three immunomodulators (immunoinhibitory, immunostimulator, MHC molecule), chemokines, and receptors. Conclusion: The results suggested that the transcript levels of COL4As could act as potential indicators for early disease progression. The expression of COL4A4 could contribute directly to disease prognosis. Besides, COL4A1/2/3/4 widely participated in tumor immunity. However, further studies are needed to confirm their clinical values in the ccRCC patients.

Background: New coronavirus disease 2019 (COVID-19) poses a severe threat to human life and causes a global pandemic. The purpose of current research is to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.Methods: We collected the number of COVID-19 confirmed cases between January 11, 2020, and c, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. Results: Daily growth of confirmed cases and Baidu index values for each COVID-19 related symptoms presented a robust positive correlation during the outbreak (fever: rs=0.705, p=9.623×10-6; cough: rs=0.592, p=4.485×10-4; fatigue: rs=0.629, p=1.494×10-4; sputum production: rs=0.648, p=8.206×10-5; shortness of breath: rs=0.656, p=6.182×10-5). The average search-to-confirmed interval is 19.8 days in China. The daily Baidu Index value's optimal time lags were the fourth day for cough, third day for fatigue, firth day for sputum production, firth day for shortness of breath, and 0 days for fever. Conclusion: Search terms of COVID-19-related symptoms on the Baidu search engine have significant correlations with confirmed cases. Since the Baidu search engine can reflect the Public's attention to the pandemic and regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

Background: New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.Methods: We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed.Results: Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623×10-6; cough: rs=0.592, p=4.485×10-4; fatigue: rs=0.629, p=1.494×10-4; sputum production: rs=0.648, p=8.206×10-5; shortness of breath: rs=0.656, p=6.182×10-5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value's optimal time lags were the four days for cough, two days for fatigue, three days for sputum production, one day for shortness of breath, and 0 days for fever.Conclusion: The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public's attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.