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Backgrounds The widespread coronavirus disease 2019 (COVID-19) outbreak impacted the mental health of infected patients admitted to Fangcang shelter hospital a large-scale, temporary structure converted from existing public venues to isolate patients with mild or moderate symptoms of COVID-19 infection. Objective This study aimed to investigate the risk factors of the infected patients from a new pharmacological perspective based on psychiatric drug consumption rather than questionnaires for the first time. Methods We summarised the medical information and analysed the prevalence proportion, characteristics, and the related risk factors of omicron variants infected patients in the Fangcang Shelter Hospital of the National Exhibition and Convention Center (Shanghai) from 9 April 2022 to 31 May 2022. Results In this study, 6,218 individuals at 3.57% of all admitted patients in the Fangcang shelter were collected suffering from mental health problems in severe conditions including schizophrenia, depression, insomnia, and anxiety who needed psychiatric drug intervention. In the group, 97.44% experienced their first prescription of psychiatric drugs and had no diagnosed historical psychiatric diseases. Further analysis indicated that female sex, no vaccination, older age, longer hospitalization time, and more comorbidities were independent risk factors for the drug-intervened patients. Conclusion This is the first study to analyse the mental health problems of omicron variants infected patients hospitalised in Fangcang shelter hospitals. The research demonstrated the necessity of potential mental and psychological service development in Fangcang shelters during the COVID-19 pandemic and other public emergency responses.
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Viral infections are known as one of the major factors causing death. Ginseng is a medicinal plant that demonstrated a wide range of antiviral potential, and saponins are the major bioactive ingredients in the genus Panax with vast therapeutic potential. Studies focusing on the antiviral activity of the genus Panax plant-derived agents (extracts and saponins) and their mechanisms were identified and summarized, including contributions mainly from January 2016 until January 2022. P. ginseng, P. notoginseng, and P. quinquefolius were included in the review as valuable medicinal herbs against infections with 14 types of viruses. Reports from 9 extracts and 12 bioactive saponins were included, with 6 types of protopanaxadiol (PPD) ginsenosides and 6 types of protopanaxatriol (PPT) ginsenosides. The mechanisms mainly involved the inhibition of viral attachment and replication, the modulation of immune response by regulating signaling pathways, including the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway, cystathionine γ-lyase (CSE)/hydrogen sulfide (H2S) pathway, phosphoinositide-dependent kinase-1 (PDK1)/ protein kinase B (Akt) signaling pathway, c-Jun N-terminal kinase (JNK)/activator protein-1 (AP-1) pathway, and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway. This review includes detailed information about the mentioned antiviral effects of the genus Panax extracts and saponins in vitro and in vivo, and in human clinical trials, which provides a scientific basis for ginseng as an adjunctive therapeutic drug or nutraceutical.
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Backgrounds: The widespread coronavirus disease 2019 (COVID-19) outbreak impacted the mental health of infected patients admitted to Fangcang shelter hospital a large-scale, temporary structure converted from existing public venues to isolate patients with mild or moderate symptoms of COVID-19 infection. Objective: This study aimed to investigate the risk factors of the infected patients from a new pharmacological perspective based on psychiatric drug consumption rather than questionnaires for the first time. Methods: We summarised the medical information and analysed the prevalence proportion, characteristics, and the related risk factors of omicron variants infected patients in the Fangcang Shelter Hospital of the National Exhibition and Convention Center (Shanghai) from 9 April 2022 to 31 May 2022. Results: In this study, 6,218 individuals at 3.57% of all admitted patients in the Fangcang shelter were collected suffering from mental health problems in severe conditions including schizophrenia, depression, insomnia, and anxiety who needed psychiatric drug intervention. In the group, 97.44% experienced their first prescription of psychiatric drugs and had no diagnosed historical psychiatric diseases. Further analysis indicated that female sex, no vaccination, older age, longer hospitalization time, and more comorbidities were independent risk factors for the drug-intervened patients. Conclusion: This is the first study to analyse the mental health problems of omicron variants infected patients hospitalised in Fangcang shelter hospitals. The research demonstrated the necessity of potential mental and psychological service development in Fangcang shelters during the COVID-19 pandemic and other public emergency responses.
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The prevention and control of communicable diseases such as COVID-19 has been a worldwide problem, especially in terms of mining towards latent spreading paths. Although some communication models have been proposed from the perspective of spreading mechanism, it remains hard to describe spreading mechanism anytime. Because real-world communication scenarios of disease spreading are always dynamic, which cannot be described by time-invariant model parameters, to remedy such gap, this paper explores the utilization of big data analysis into this area, so as to replace mechanism-driven methods with big data-driven methods. In modern society with high digital level, the increasingly growing amount of data in various fields also provide much convenience for this purpose. Therefore, this paper proposes an intelligent knowledge discovery method for critical spreading paths based on epidemic big data. For the major roadmap, a directional acyclic graph of epidemic spread was constructed with each province and city in mainland China as nodes, all features of the same node are dimension-reduced, and a composite score is evaluated for each city per day by processing the features after principal component analysis. Then, the typical machine learning model named XGBoost carries out processing of feature importance ranking to discriminate latent candidate spreading paths. Finally, the shortest path algorithm is used as the basis to find the critical path of epidemic spreading between two nodes. Besides, some simulative experiments are implemented with use of realistic social network data. [ FROM AUTHOR]
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The Chinese government recently proposed ammonia (NH3) emission reductions (but without a specific national target) as a strategic option to mitigate fine particulate matter (PM2.5) pollution. We combined a meta-analysis of nationwide measurements and air quality modeling to identify efficiency gains by striking a balance between controlling NH3 and acid gas (SO2 and NOx) emissions. We found that PM2.5 concentrations decreased from 2000 to 2019, but annual mean PM2.5 concentrations still exceeded 35 µg m-3 at 74 % of 1498 monitoring sites during 2015–2019. The concentration of PM2.5 and its components were significantly higher (16 %–195 %) on hazy days than on non-hazy days. Compared with mean values of other components, this difference was more significant for the secondary inorganic ions SO42-, NO3-, and NH4+ (average increase 98 %). While sulfate concentrations significantly decreased over this period, no significant change was observed for nitrate and ammonium concentrations. Model simulations indicate that the effectiveness of a 50 % NH3 emission reduction for controlling secondary inorganic aerosol (SIA) concentrations decreased from 2010 to 2017 in four megacity clusters of eastern China, simulated for the month of January under fixed meteorological conditions (2010). Although the effectiveness further declined in 2020 for simulations including the natural experiment of substantial reductions in acid gas emissions during the COVID-19 pandemic, the resulting reductions in SIA concentrations were on average 20.8 % lower than those in 2017. In addition, the reduction in SIA concentrations in 2017 was greater for 50 % acid gas reductions than for the 50 % NH3 emission reductions. Our findings indicate that persistent secondary inorganic aerosol pollution in China is limited by emissions of acid gases, while an additional control of NH3 emissions would become more important as reductions of SO2 and NOx emissions progress.
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Despite large decreases of emissions of air pollution during the coronavirus disease 2019 (COVID-19) lockdown in 2020, an unexpected regional severe haze has still occurred over the North China Plain. To clarify the origin of this pollution, we studied air concentrations of fine particulate matter (PM2.5), NO2, O3, PM10, SO2, and CO in Beijing, Hengshui and Baoding during the lockdown period from January 24 to 29, 2020. Variations of PM2.5 composition in inorganic ions, elemental carbon and organic matter were also investigated. The HYSPLIT model was used to calculate backward trajectories and concentration weighted trajectories. Results of the cluster trajectory analysis and model simulations show that the severe haze was caused mainly by the emissions of northeastern non-stopping industries located in Inner Mongolia, Liaoning, Hebei, and Tianjin. In Beijing, Hengshui and Baoding, the mixing layer heights were about 30% lower and the maximum relative humidity was 83% higher than the annual averages, and the average wind speeds were lower than 1.5 m s-1. The concentrations of NO3 -, SO4 2-, NH4 +, organics and K+ were the main components of PM2.5 in Beijing and Hengshui, while organics, K+, NO3 -, SO4 2-, and NH4 + were the main components of PM2.5 in Baoding. Contrary to previous reports suggesting a southerly transport of air pollution, we found that northeast transport caused the haze formation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10311-021-01314-8.
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Despite large decreases of emissions of air pollution during the coronavirus disease 2019 (COVID-19) lockdown in 2020, an unexpected regional severe haze has still occurred over the North China Plain. To clarify the origin of this pollution, we studied air concentrations of fine particulate matter (PM2.5), NO2, O3, PM10, SO2, and CO in Beijing, Hengshui and Baoding during the lockdown period from January 24 to 29, 2020. Variations of PM2.5 composition in inorganic ions, elemental carbon and organic matter were also investigated. The HYSPLIT model was used to calculate backward trajectories and concentration weighted trajectories. Results of the cluster trajectory analysis and model simulations show that the severe haze was caused mainly by the emissions of northeastern non-stopping industries located in Inner Mongolia, Liaoning, Hebei, and Tianjin. In Beijing, Hengshui and Baoding, the mixing layer heights were about 30% lower and the maximum relative humidity was 83% higher than the annual averages, and the average wind speeds were lower than 1.5 m s−1. The concentrations of NO3−, SO42−, NH4+, organics and K+ were the main components of PM2.5 in Beijing and Hengshui, while organics, K+, NO3−, SO42−, and NH4+ were the main components of PM2.5 in Baoding. Contrary to previous reports suggesting a southerly transport of air pollution, we found that northeast transport caused the haze formation. Supplementary Information The online version contains supplementary material available at 10.1007/s10311-021-01314-8.
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Summary Transportation contributes to around one-fifth of global greenhouse gas emissions, while also causing severe air pollution. The conversion to electric vehicles (EVs) represents a major path to decarbonize the transport sector, with potentially significant co-benefits for human health. However, the scale of such co-benefits largely remains an empirical question and lacks observational evidence. The full lockdown in China during the coronavirus disease 2019 (COVID-19) pandemic provides an unprecedented real-world experiment to evaluate emission reduction potentials of a large-scale transition to EVs. Here, we utilize ground and satellite observations of air quality during the full lockdown to constrain predictions of a comprehensive chemical transport model and find that the substantial traffic reductions are near-linearly linked to reductions of PM2.5 (particles with an aerodynamic diameter ≤2.5 μm) and NO2. A further extrapolation of a full conversion to EVs shows a significant reduction of PM2.5 (30%–70%) and NO2 (30%–80%) in most of China. Our findings provide fact-based evidence of potential environmental benefits generated by fully switching to EVs.
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One of the ubiquitous human behaviours observed in natural disasters and humanitarian crisis is irrational stockpiling (also known as hoarding or panic buying). Limited, distorted and exaggerated information during crisis disturbs people's judgement and results in aberrant actions which can be explained with economics and psychology theories. The objective of this paper is to examine the perplexing stockpiling phenomena during disasters like COVID-19 pandemic and discuss its immediate and long-term impact on economy, society and local communities.
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The outbreak of coronavirus named COVID-19, initially identified in Wuhan, China in December 2019, has spread rapidly at the global scale. Most countries have rapidly stopped almost all activities including industry, services and transportation of goods and people, thus decreasing air pollution in an unprecedented way, and providing a unique opportunity to study air pollutants. While satellite data have provided visual evidence for the global reduction in air pollution such as nitrogen dioxide (NO2) worldwide, precise and quantitative information is missing at the local scale. Here we studied changes in particulate matter (PM2.5, PM10), carbon monoxide (CO), NO2, sulfur dioxide (SO2) and ozone (O3) at 10 urban sites in Hangzhou, a city of 7.03 million inhabitants, and at 1 rural site, before city lockdown, January 1-23, during city lockdown, January 24-February 15, and during resumption, February 16-28, in 2020. Results show that city lockdown induced a sharp decrease in PM2.5, PM10, CO, and NO2 concentrations at both urban and rural sites. The NO2 decrease is explained by reduction in traffic emissions in the urban areas, and by lower regional transport in rural areas during lockdown, as expected. SO2 concentrations decreased from 6.3 to 5.3 µg m-3 in the city, but increased surprisingly from 4.7 to 5.8 µg m-3 at the rural site: this increase is attributed both to higher coal consumption for heating and emissions from traditional fireworks of the Spring Eve and Lantern Festivals during lockdown. Unexpectedly, O3 concentrations increased by 145% from 24.6 to 60.6 µg m-3 in the urban area, and from 42.0 to 62.9 µg m-3 in the rural area during the lockdown. This finding is explained by the weakening of chemical titration of O3 by NO due to reductions of NOx fresh emissions during the non-photochemical reaction period from 20:00 PM to 9:00 AM (local time). During the lockdown, compared to the same period in 2019, the daily average concentrations in the city decreased by 42.7% for PM2.5, 47.9% for PM10, 28.6% for SO2, 22.3% for CO and 58.4% for NO2, which is obviously explained by the absence of city activities. Overall, we observed not only the expected reduction in some atmospheric pollutants (PM, SO2, CO, NO2), but also unexpected increases in SO2 in the rural areas and of ozone (O3) in both urban and rural areas, the latter being paradoxically due to the reduction in nitrogen oxide levels. In other words, the city lockdown has improved air quality by reducing PM2.5, PM10, CO, and NO2, but has also decreased air quality by augmenting O3 and SO2.