ABSTRACT
Objective To provide a system for warning, preventing and controlling emerging infectious diseases from a macroscopic perspective, using the COVID-19 epidemic data and effective distance model. Methods The dates of hospitalization/isolation treatment of the first confirmed cases of COVID-19 and the cumulative numbers of confirmed cases in different provinces in China reported as of 23 February, 2020 were collected. The Location Based Service (LBS) big data platform of 'Baidu Migration' was employed to obtain the data of the proportion of the floating population from Wuhan to all parts of the country. Effective distance models and linear regression models were established to analyze the relationship between the effective distance and the arrival time of the epidemic as well as the number of cumulative confirmed cases at provincial and municipal levels. Results The arrival time of the epidemic and the cumulative number of confirmed cases of COVID-19 had significant linear relationship at both provincial and municipal levels in China, and the regression coefficients of each linear model were significant ( P <0.001). At the provincial level, the effective distance could explain about 71% of the variation of the model with arrival time along with around 90% of the variation for the model in the cumulative confirmed case magnitude; at the municipal level, the effective distance could explain about 66% of the variation for the model in arrival time, and about 85% of the variation of the model with the cumulative confirmed case magnitude. Conclusions The fitting degree of the models are good. The LBS big data and effective distance model can be used to estimate the track, time and extent of epidemic spread to provide useful reference for early warning, prevention and control of emerging infectious diseases.
ABSTRACT
<p><b>BACKGROUND</b>Advances in the understanding of cardiovascular pathogenesis have highlighted that inflammation plays a central role in atherosclerotic coronary heart disease. Therefore, exploring pharmacologically based anti-inflammatory treatments to be used in cardiovascular therapeutics is worthwhile to promote the discovery of novel ways of treating cardiovascular disorders.</p><p><b>METHODS</b>The myocardial cell line H9c2(2-1) was exposed to lipopolysaccharide (LPS) in culture and resulted in a cellular pro-inflammation status. miR-21 microRNA levels were detected using quantitative real-time polymerase chain reaction (Q-RT-PCR). The influence of lovastatin on miR-21 under normal and pro-inflammatory conditions was tested after being added to the cell culture mixture for 24 hours. Conditional gene function of two predicted cardiovascular system relevant downstream targets of miR-21, protein phosphatase 1 regulatory subunit 3A (PPP1R3A) and signal transducer and activator of transcription 3 (STAT3), were analyzed with immunoblotting.</p><p><b>RESULTS</b>Forty-eight hours of LPS treatment significantly increased the miR-21 to 170.71%± 34.32% of control levels (P = 0.002). Co-treatment with lovastatin for 24 hours before harvesting attenuated the up-regulation of miR-21 (P = 0.013). Twenty-four hours of lovastatin exposure up-regulated PPP1R3A to 143.85%± 21.89% of control levels in cardiomyocytes (P = 0.023). Lovastatin up-regulated the phosphorylation level of STAT3 compared to the background LPS pretreatment (P = 0.0077), this effect was significantly (P = 0.018) blunted when miR-21 was functionally inhibited.</p><p><b>CONCLUSIONS</b>miR-21 plays a major role in the regulation of the cellular anti-inflammation effects of lovastatin.</p>