ABSTRACT
In response to the construction process of Healthy China. it is rather important to create a safe, healthy and energy-efficient indoor environment for public buildings. The public building space is often densely populated, with a large flow of people and many types of air pollution, which presents non-uniform dynamic distribution characteristics. This brings great challenges to the control of indoor air safety, especially during the pandemic period of COVID-19. Excessive ventilation may not only cause large energy waste. but also lead to cross-contamination and even a cluster of infection. In this paper, an operation and maintenance (O&M) control system for indoor air safety is developed based on the core concepts and basic methods of human ergonomics. In this system, one of the important human environmental variables is focused for control, i.e.. indoor air pollution level. Especially after the outbreak of COVID-19. droplets and droplet nuclei from respiration are the most significant air pollution categories required for mitigation. Towards the efficient control of air pollution in large public buildings. it should further take into account the interaction of human, equipment and machines (i.e., ventilation_ air purification and disinfection and intelligent control system) and building environment. Firstly, on the basis of the online monitoring of indoor air pollution concentration and personnel flow, the non-uniform dynamic distribution of indoor pollutants and personnel can be obtained by using the non-uniform and low-dimensional rapid prediction models and computer vision processing. Then, the optimal setting results of ventilation parameters (e.g., ventilation modes, supply air rate. etc.) can be outputted by the environmental control decision system. Finally, based on a combination of monitoring sensors, controllers and actuator hardware equipment (at the location of fans or dampers), the intelligent regulation and control of ventilation system can be realized, aimed at minimizing energy consumption and reducing pollutant concentration and exposure level. Meanwhile, the air purification and disinfection system (especially for the disinfection of virus particles) are operated under the condition of the ventilated environment, which can serve as a powerful auxiliary to the maintenance of indoor air safety. The workflow and effect of the O&M control system are demonstrated by an engineering application case of the front hall in the International Convention and Exhibition Center. The results indicate that the non-uniform and low-dimensional rapid prediction model for pollutant concentration is effective for the ventilation control with the average prediction difference of 11.9%. The implementation of the intelligent ventilation system can reduce the risk of human infection to less than 4%. and its energy-saving ratio for the ventilation can be as high as about 45%. Through optimizing the layout strategies of disinfection devices based on the intelligent ventilation control, the space accessibility of negative oxygen ions can be well accepted, to further increase the removal efficiency of air pollution. The calculated value of space disinfection rate is more than 99%, which can further reduce the risk of infection by 1-2 orders of magnitude. This study can provide an important reference for the promotion and upgrading of O&M control system for indoor air safety.
ABSTRACT
Presently, the coronavirus disease 2019 (COVID-19) has infected more than 200 million of the world's population and has killed more than 4 million people. In addition to reverse transcription nucleic acid polymerase chain reaction (RT-PCR) as the main detection method, the deep learning-based method using diagnose X-ray or CT scans has become an promising alternative. Last years, Convolution neural network (CNN) has became the methodology choices in the field of medical images until the emergence of Vision Transformer (ViT) broke this situation. Transformer gradually dominates in the field of computer vision, but Transformer lacks inductive biases of convolution operation, requires a lot of data to achieve better performance than CNN, and the amount of calculation is too large when the input is a high-resolution picture. It is found that Transformer and CNN can complement each other. Therefore, there are many kinds of research on the combination of them. However, there is little research on the hybrid model's diagnostic direction of medical images, especially COVID-19 image classification. For this problem, we search the way of marrying CNN and Transformer and propose a hybrid model combining CNN and Transformer, which we called DenseTransformer. Experiments on our COVID-19 CT scans dataset show that the hybrid model, which combines CNN and Transformer properly, can perform better than pure CNN and pure Transformer in the COVID-19 image classification task, and the performance will be further improved after using self-supervised learning. © 2022 IEEE.
ABSTRACT
The impact of coronavirus disease 2019 (COVID-19) on endometriosis (EM) is currently unclear. Here, we aimed to describe the potential influence of COVID-19 on the pathogenesis, clinical symptoms, and treatment of EM. The cytokine storm caused by COVID-19 may induce the occurrence and progression of EM, and immunosuppression of COVID-19 may help the ectopic endometrium escape from immune clearance. Consequently, the forced social isolation and the cancelation of non-emergency medical treatment during the COVID-19 pandemic aggravate anxiety and psychological pressure, which can aggravate the symptoms related to EM and delay routine medical services.
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Objective: The Chinese herbal formula Huo-Xiang-Zheng-Qi (HXZQ) is effective in preventing and treating coronavirus disease 19 (COVID-19) infection;however, its mechanism remains unclear. This study used network pharmacology and molecular docking techniques to investigate the mechanism of action of HXZQ in preventing and treating COVID-19. Methods: The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to search for the active ingredients and targets of the 10 traditional Chinese medicines (TCMs) of HXZQ prescription (HXZQP). GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmacogenomics Knowledge Base (PharmGKB), Therapeutic Target Database (TTD), and DrugBank databases were used to screen COVID-19-related genes and intersect them with the targets of HXZQP to obtain the drug efficacy targets. Cytoscape 3.8 software was used to construct the drug-active ingredient–target interaction network of HXZQP and perform protein–protein interaction (PPI) network construction and topology analysis. R software was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, AutoDock Vina was utilized for molecular docking of the active ingredients of TCM and drug target proteins. Results: A total of 151 active ingredients and 250 HXZQP targets were identified. Among these, 136 active ingredients and 67 targets of HXZQP were found to be involved in the prevention and treatment of COVID-19. The core proteins identified in the PPI network were MAPK1, MAPK3, MAPK8, MAPK14, STAT3, and PTGS2. Using GO and KEGG pathway enrichment analysis, HXZQP was found to primarily participate in biological processes such as defense response to a virus, cellular response to biotic stimulus, response to lipopolysaccharide, PI3K-Akt signaling pathway, Th17 cell differentiation, HIF-1 signaling pathway, and other signaling pathways closely related to COVID-19. Molecular docking results reflected that the active ingredients of HXZQP have a reliable affinity toward EGFR, MAPK1, MAPK3, MAPK8, and STAT3 proteins. Conclusion: Our study elucidated the main targets and pathways of HXZQP in the prevention and treatment of COVID-19. The study findings provide a basis for further investigation of the pharmacological effects of HXZQP.
ABSTRACT
In response to the construction process of Healthy China. it is rather important to create a safe, healthy and energy-efficient indoor environment for public buildings. The public building space is often densely populated, with a large flow of people and many types of air pollution, which presents non-uniform dynamic distribution characteristics. This brings great challenges to the control of indoor air safety, especially during the pandemic period of COVID-19. Excessive ventilation may not only cause large energy waste. but also lead to cross-contamination and even a cluster of infection. In this paper, an operation and maintenance (O&M) control system for indoor air safety is developed based on the core concepts and basic methods of human ergonomics. In this system, one of the important human environmental variables is focused for control, i.e.. indoor air pollution level. Especially after the outbreak of COVID-19. droplets and droplet nuclei from respiration are the most significant air pollution categories required for mitigation. Towards the efficient control of air pollution in large public buildings. it should further take into account the interaction of human, equipment and machines (i.e., ventilation_ air purification and disinfection and intelligent control system) and building environment. Firstly, on the basis of the online monitoring of indoor air pollution concentration and personnel flow, the non-uniform dynamic distribution of indoor pollutants and personnel can be obtained by using the non-uniform and low-dimensional rapid prediction models and computer vision processing. Then, the optimal setting results of ventilation parameters (e.g., ventilation modes, supply air rate. etc.) can be outputted by the environmental control decision system. Finally, based on a combination of monitoring sensors, controllers and actuator hardware equipment (at the location of fans or dampers), the intelligent regulation and control of ventilation system can be realized, aimed at minimizing energy consumption and reducing pollutant concentration and exposure level. Meanwhile, the air purification and disinfection system (especially for the disinfection of virus particles) are operated under the condition of the ventilated environment, which can serve as a powerful auxiliary to the maintenance of indoor air safety. The workflow and effect of the O&M control system are demonstrated by an engineering application case of the front hall in the International Convention and Exhibition Center. The results indicate that the non-uniform and low-dimensional rapid prediction model for pollutant concentration is effective for the ventilation control with the average prediction difference of 11.9%. The implementation of the intelligent ventilation system can reduce the risk of human infection to less than 4%. and its energy-saving ratio for the ventilation can be as high as about 45%. Through optimizing the layout strategies of disinfection devices based on the intelligent ventilation control, the space accessibility of negative oxygen ions can be well accepted, to further increase the removal efficiency of air pollution. The calculated value of space disinfection rate is more than 99%, which can further reduce the risk of infection by 1-2 orders of magnitude. This study can provide an important reference for the promotion and upgrading of O&M control system for indoor air safety.
ABSTRACT
Since the spread of the COVID-19 virus among people all around the world, authorities have adopted various policies against it. Effective policies are usually based on strong academic support. Scholars have done a lot of research on the development of the virus and the epidemic. Compartmental models are widely used to solve infectious diseases related problems. Based on the extensive research on the COVID-19 virus by previous scholars, it is verified that there is a significant latency period when susceptible are transferred into exposed. Hence, the SEIR model is widely used to model the COVID-19 epidemic. And in this paper, we use a revised SEIR model to highlight the significance of the limitation term, analyzing the trend of the epidemic under the different methods of isolations. The research data is based on the outbreak of the epidemic in Nanjing in July 2021. The paper examines that isolation is one of the most effective measures to cut off the transmission route of COVID-19. © 2022 SPIE
ABSTRACT
Background. Casirivimab and imdevimab (CAS/IMDEV) is authorized for emergency use in the US for outpatients with COVID-19. We present results from patient cohorts receiving low flow or no supplemental oxygen at baseline from a phase 1/2/3, randomized, double-blinded, placebo (PBO)-controlled trial of CAS/IMDEV in hospitalized patients (pts) with COVID-19. Methods. Hospitalized COVID-19 pts were randomized 1:1:1 to 2.4 g or 8.0 g of IV CAS/IMDEV (co-administered) or PBO. Primary endpoints were time-weighted average (TWA) change in viral load from baseline (Day 1) to Day 7;proportion of pts who died or went on mechanical ventilation (MV) through Day 29. Safety was evaluated through Day 57. The study was terminated early due to low enrollment (no safety concerns). Results. Analysis was performed in pooled cohorts (low flow or no supplemental oxygen) as well as combined treatment doses (2.4 g and 8.0 g). The prespecified primary virologic analysis was in seronegative (seroneg) pts (combined dose group n=360;PBO n=160), where treatment with CAS/IMDEV led to a significant reduction in viral load from Day 1-7 (TWA change: LS mean (SE): -0.28 (0.12);95% CI: -0.51, -0.05;P=0.0172;Fig. 1). The primary clinical analysis had a strong positive trend, though it did not reach statistical significance (P=0.2048), and 4/6 clinical endpoints prespecified for hypothesis testing were nominally significant (Table 1). In seroneg pts, there was a 47.0% relative risk reduction (RRR) in the proportion of pts who died or went on MV from Day 1-29 (10.3% treated vs 19.4% PBO;nominal P=0.0061;Fig. 2). There was a 55.6% (6.7% treated vs 15.0% PBO;nominal P=0.0032) and 35.9% (7.3% treated vs 11.5% PBO;nominal P=0.0178) RRR in the prespecified secondary endpoint of mortality by Day 29 in seroneg pts and the overall population, respectively (Fig. 2). No harm was seen in seropositive patients, and no safety events of concern were identified. Conclusion. Co-administration of CAS/IMDEV led to a significant reduction in viral load in hospitalized, seroneg pts requiring low flow or no supplemental oxygen. In seroneg pts and the overall population, treatment also demonstrated clinically meaningful, nominally significant reductions in 28-day mortality and proportion of pts dying or requiring MV.
ABSTRACT
The flood of the Yangtze River has the characteristics of high peak, large quantity and long duration. The Yangtze River Hydrology Bureau summarizes and combs the complete business process chain of flood hydrological monitoring, and gradually constructs the Yangtze River flood hydrological monitoring system. Including station network layout, early warning response, monitoring technology, information processing, results output and other dimensions. The hydrological monitoring system of the Yangtze River flood has been gradually constructed and has been successfully applied in many flood basins. Especially under the special situation of COVID-19 epidemic situation in 2020 and the severe flood situation in the Yangtze River Basin, the scientific and practical nature and practicability of the hydrological monitoring system of the Yangtze River flood are further verified. In view of the shortcomings existing in the existing monitoring system, this paper looks forward to the frontier technologies involved in flood monitoring, and has a certain reference function for flood hydrological emergency monitoring. © 2021 The authors and IOS Press.
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Objective: To evaluate COVID-19-related persistent chemosensory dysfunction (CD) in a cohort of Quebec healthcare workers. Background: CD is now recognized as a major symptom of COVID-19. While published studies have investigated and quantified persistent CD in up to 20% of patients, very few have examined the duration, severity and trajectory of chemosensory impairments in patients with persisting CD. Design/Methods: We conducted a cross-sectional observational study in a cohort of over 800 healthcare workers who received a positive diagnosis for SARS-CoV-2 with a nasopharyngeal viral swab, recruited through the Quebec National Institute of Public Health, 4 months after diagnosis. We used an online 64-item questionnaire examining self-evaluated olfactory, gustatory and trigeminal impairments as well as clinical and epidemiological consequences of the infection which includes a previously validated CD-home test (CD-HT). As part of the questionnaire, both smell and taste were evaluated on a scale from 0 to 10 (0: No perception;10: Very strong perception). Results: 813 respondents (women: 84.1%) answered the questionnaire on average 150.1 (SD: 31.1) days post-diagnosis. Average self-reported smell ratings were 8.98 (1.62) pre-infection, 2.85 (3.74) during the acute phase and 7.41 (2.46) when the respondents answered the questionnaire. These numbers were 9.20 (1.34), 3.59 (3.67), and 8.05 (2.20) for taste. In 458 respondents who indicated a compromised sense of smell during the acute phase, average smell rating at the time they answered the questionnaire was 6.89 (2.52) compared to 9.03 (1.61) before the infection.297 (51.2%) of them reported not regaining olfactory functions at the time of testing;when assessed with the CD-HT, 134 of 810 respondents (18.4%) have persistent loss of smell. No significant sex differences were observed in acute or persistent smell loss. Conclusions: CD persists in a significant number of COVID-19 patients. Long-term follow-up and in-laboratory chemosensory examinations are required to assess the extent of the associated impairments.
ABSTRACT
COVID-19 is threatening the health of the entire human population. In order to control the spread of the disease, epidemiological investigations should be conducted, to trace the infection source of each confirmed patient and isolate their close contacts. However, the analysis on a mass of case reports in epidemiological investigation is extremely time-consuming and labor-intensive. This paper presents an end-to-end framework for automatic epidemiological case report analysis and inference, in which a Tuple-based Multi-Task Neural Network (TMT-NN) is designed and implemented for jointly recognizing epidemiological entities and relations from case reports, and an epidemiological knowledge graph and its corresponding inference engine are built to uncover the infection modes, sources and pathways. Preliminary experiments demonstrate the promising results, and we published a real data set of COVID-19 epidemiological investigation corpora at Github, as well as contributing our COVID-19 epidemiological knowledge graph to the open community OpenKG.cn.
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Objective: To investigate the positive rate for 2019-nCoV tests and co-infections in Wuhan district. Methods: A total of 8 274 cases in Wuhan were enrolled in this cross-sectional study during January 20 to February 9 in 2020, and were tested for 2019-nCoV using fluorescence quantitative PCR. Both respiratory tract samples (nasopharynx, oropharynx, sputum and alveolar lavage fluid) and non-respiratory tract samples (urine, feces, anal swabs, blood and conjunctival sac swabs) were collected. If both orf1ab and N genes are positive, they are classified as nucleic acid test positive group;if both orf1ab and N genes are negative, they are classified as negative group;if single gene target is positive, they are classified as suspicious group. Individuals were divided into male group and female group according to sex. At the same time, 316 patients were tested for 13 respiratory pathogens by multiplex PCR. Results: Among the 8 274 subjects, 2 745 (33.17%) were 2019-nCoV infected;5 277 (63.77%) subjects showed negative results in the 2019-nCoV nucleic acid test;and 252 cases (3.05%) was not definitive (inconclusive result). The age of cases with COVID-19 patients and inconclusive cases was significantly higher than that of cases without 2019-nCoV infection (56>40, t=27.569, P<0.001;52>40, t=6.774, P<0.001). The positive rate of 13 respiratory pathogens multiple tests was significantly lower in 104 subjects who were positive for 2019-nCoV compared with those in subjects who were negative for 2019-nCoV test (5.77% vs 18.39%, χ2=24.105, P=0.003). Four types of respiratory tract samples and five types of non-respiratory tract sampleswere found to be positive for 2019-nCoV nucleic acid test. Conclusion: The 2019-nCoV nucleic acid positive rate inmale is higher than infemale. Co-infections should be pay close attention in COVID-19 patients. 2019-nCoV nucleic acid can be detected in non-respiratory tract samples.