Although it is widely acknowledged that environmental concerns can reduce PM2.5 pollution, few studies have empir-ically estimated whether environmental concerns could bring health benefits by mitigating PM2.5 polluti...
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Although it is widely acknowledged that environmental concerns can reduce PM2.5 pollution, few studies have empir-ically estimated whether environmental concerns could bring health benefits by mitigating PM2.5 pollution. Here, we quantified government and media environmental concerns with text-mining algorithm, matched with cohort data along with high-resolution gridded PM2.5 data. Accelerated failure time model and mediation model were used to ex-plore the association between PM2.5 exposure and onset time to cardiovascular events, and the mitigation effect of en-vironmental concerns. Every 1 mu g/m3 increase in PM2.5 exposure was associated with shortened time to stroke and heart problem, with time ratios of 0.9900 and 0.9986, respectively. Each 1 unit increase in government and media envi-ronmental concerns, as well as their synergistic effect decreased PM2.5 pollution by 0.32 %, 0.25 % and 0.46 %, respec-tively;and decrease PM2.5 resulted in prolonged onset time to cardiovascular events. Mediation analysis revealed that reduced PM2.5 mediated up to 33.55 % of the association between environmental concerns and onset time to cardiovas-cular events, suggesting that other mediation pathways were also possible. Associations of PM2.5 exposure and environ-mental concerns with stroke and heart problem were similar in different subgroups. Overall, environmental concerns reduce risks of cardiovascular disease by mitigating PM2.5 pollution and other pathways in a real-world data set. This study provides insights for low-and-middle-income countries to address air pollution and improve health co-benefit.
Understanding the role of genetics is very important for the in-depth study of a disease. Even though lots of information about gene-disease association is available, it is difficult even for an expert user to manuall...
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ISBN:
(纸本)9781479960132
Understanding the role of genetics is very important for the in-depth study of a disease. Even though lots of information about gene-disease association is available, it is difficult even for an expert user to manually extract it from the huge volume of literature. Therefore, this work introduces a novel extraction tool that can identify disease associated genes from the literature using text-mining algorithm. Here, Hidden Markov Model is combined with a rule-based Named Entity Recognition approach to identify gene symbols from the literature. This will predict the good candidate genes for the disease which will help in the further analysis of the disease.
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