Out-of-hospital management strategies for gastric cancer patients during the COVID-19 outbreak.

Since the outbreak of Corona Virus Disease 2019 occurred in December 2019, the reduction of population mobility has curbed the spread of the epidemic to some extent but also prolonged the waiting time for the treatment of patients with gastric cancer. Based on fully understanding the different staging characteristics of gastric cancer, clinical departments should develop reasonable out-of-hospital management strategies. On one hand, reasonable communication channels should be established to allow patients to receive adequate guidance out of the hospital. On the other hand, shared decisions with patients should be made to adjust treatment strategies, and education on viral prevention should be implemented to minimize the impact of the epidemic on tumor treatment.Copyright © 2020 by the Chinese Medical Association.

Filtration performance of new reduced graphene oxide air filter material against bacteria in the atmosphere during the initial stage of heating

With the large-scale outbreak of the COVID-19, people have gradually realized the importance of bioaerosols in the environment, and how to efficiently filter out microbial aerosols in the air, so as to create a safe and healthy air environment is urgent. The non-bacteriostatic F6 non-woven filter material and the synthesized new reduced graphene oxide air filter were tested and analyzed in this paper, and the filtration performance of the material against bacterial aerosols in the atmosphere at the initial stage of heating. The results showed that during the initial stage of heating, the particle size distributions of aerosols in the atmosphere during working days were stageⅠ(>7.0μm)4.34%, stageⅡ(4.7~7.0μm)4.62%, stageⅢ(3.3~4.7μm)13.30%, stageⅣ(2.1~3.3μm)21.11%, stageⅤ(1.1~2.1μm)38.70%, stageⅥ(0.65~1.1μm)17.92%. The particle size distributions of aerosols in the atmosphere on non-working days were stageⅠ(>7.0μm)4.52%, stageⅡ(4.7~7.0μm)13.66%, stageⅢ(3.3~4.7μm)23.04%, stageⅣ(2.1~3.3μm)31.82%, stageⅤ(1.1~2.1μm)15.18%, stageⅥ (0.65~1.1μm)11.78%. The new reduced graphene oxide filter material had a 10% increase in the filtration efficiency of the total bacterial aerosol compared with the ordinary non-woven filter material. Among them, the filtration efficiency of the respirable bacterial aerosol (particle size <4.7μm) was significantly improved by 40%. The results of this study could provide a certain reference for building a safe interior in the post-epidemic era, and also provided reference value for the research and development of functional air filters. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)

Complex Network Variability Analysis with Impact of Major Events on Aviation Networks

Global aviation data has complex, large and fast-updated characteristics which it's not suitable for directly building complex networks. It's challenging to assess the stability of a particular airport by analyzing the aviation network. This paper presents Complex Network Variability Analysis (CNVA) to analyze the stability of the nodes in the aviation network, which face to international incidents such as COVID-19. Firstly, CNVA cleans the data with missing information to solve the problem of data redundancy. We compress flights' data which have same origin and destination and count their number. Then, CNVA calculates all nodes' harmonic closeness centrality, betweenness centrality and eigen centrality of the data in each month, which can analyze the significance features of each months' network. Next, we calculate the centrality's average rate of variety, and we use these data's average of the absolute values as parameter value. The parameters are used to construct a vector function which called V function. HCC, BC and EC are the variables of V function. Finally, we evaluate whether a node has a large change during COVID-19 period, we calculate the difference between the average change rate of the node's V value and the average change rate of the global function V, and we evaluate its size. To verify the credibility of CNVA, we search for nodes with a higher degree of centrality for CNVA method verification. The results show that the node's V constant value greater than V function constant which have better fluctuations when they face to COVID-19. Our analysis illustrates that CNVA can better assess the stability of airports in response to major incidents when the raw data sources are sufficient. © 2021 IEEE.

Investigation and prediction of Energy consumption at St. Olays Hospital

The objective of this study is to evaluate and predict the energy use in different buildings during COVID-19 pandemic period at St. Olays Hospital in Trondheim. Based on machine learning, operational data from St. Olays hospital combined with weather data will be used to predict energy use for the hospital. Analysis of the energy data showed that the case buildings at the hospital did not have any different energy use during the pandemic this year compared to the same period last year, except for the lab center. The energy consumption of electricity, heating and cooling is very similar both in 2019 and 2020 for all buildings, but in 2020 during the pandemic, the lab center had a reduction of 35% in electricity, compared to last year. An analysis of the energy needed for heating and cooling in the end of June to the end of November was also calculated for operating room 1 and was estimated to 256 kWh/m2 for operation room 1. The machine learning algorithms perform very well to predict the energy consumption of case buildings, Random Forest and AdaBoost proves as the best models, with less than 10% margin of error, some of the models have only 4% error. An analysis of the effect of humidification of ventilation air on energy consumption in operating room 1 was also carried out. The impact on energy consumption were high in winter and will at the coldest periods be able to double the energy consumption needed in the ventilation.

Evaluation of pulmonary ventilation in COVID-19 patients using oxygen-enhanced three-dimensional ultrashort echo time MRI: a preliminary study.

AIM: To evaluate the lung function of coronavirus disease 2019 (COVID-19) patients using oxygen-enhanced (OE) ultrashort echo time (UTE) MRI. MATERIALS AND METHODS: Forty-nine patients with COVID-19 were included in the study. The OE-MRI was based on a respiratory-gated three-dimensional (3D) radial UTE sequence. For each patient, the percent signal enhancement (PSE) map was calculated using the expression PSE = (S100% - S21%)/S21%, where S21% and S100% are signals acquired during room air and 100% oxygen inhalation, respectively. Agreement of lesion detectability between UTE-MRI and computed tomography (CT) was performed using the kappa test. The Mann-Whitney U-test was used to evaluate the difference in the mean PSE between mild-type COVID-19 and common-type COVID-19. Spearman's test was used to assess the relationship between lesion mean PSE and lesion size. Furthermore, the Mann-Whitney U-test was used to evaluate the difference in region of interest (ROI) mean PSE between normal pulmonary parenchyma and lesions. The Kruskal-Wallis test was applied to test the difference in the mean PSE between different lesion types. RESULTS: CT and UTE-MRI reached good agreement in lesion detectability. Ventilation measures in mild-type patients (5.3 ± 5.5%) were significantly different from those in common-type patients (3 ± 3.9%). Besides, there was no significant correlation between lesion mean PSE and lesion size. The mean PSE of COVID-19 lesions (3.2 ± 4.9%) was significantly lower than that of the pulmonary parenchyma (5.4 ± 3.9%). No significant difference was found among different lesion types. CONCLUSION: OE-UTE-MRI could serve as a promising method for the assessment of lung function or treatment management of COVID-19 patients.

Out-of-hospital management strategies for gastric cancer patients during the COVID-19 outbreak

Since the outbreak of Corona Virus Disease 2019 occurred in December 2019, the reduction of population mobility has curbed the spread of the epidemic to some extent but also prolonged the waiting time for the treatment of patients with gastric cancer. Based on fully understanding the different staging characteristics of gastric cancer, clinical departments should develop reasonable out-of-hospital management strategies. On one hand, reasonable communication channels should be established to allow patients to receive adequate guidance out of the hospital. On the other hand, shared decisions with patients should be made to adjust treatment strategies, and education on viral prevention should be implemented to minimize the impact of the epidemic on tumor treatment. Copyright © 2020 by the Chinese Medical Association.