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Objective To identify the subtypes of cuproptosis-related genes associated with COVID-19 and construct prediction models with multiple machine learning methods.Methods Whole-genome sequencing data of normal people and COVID-19 patients (GSE163151 dataset, GSE156063 dataset) were downloaded from GEO database, and the differentially expressed cuproptosis-related genes were screened. Consistent clustering typing was used to divide the COVID-19 patients into two types (C1, C2). The correlation between the infiltration of 22 kinds of immune cells and the expression of cuproptosis-related genes in different typing COVID-19 patients was studied. GSVA method was used to study the gene variation set of different typing COVID-19 patients. WGCNA clustering method was used to construct gene co-expression network between normal people and COVID-19 patients, and between different typing (C1, C2) COVID-19 patients respectively, and the gene modules with the largest correlation coefficient and statistical significance were selected to screen out the key genes. Multiple machine learning methods (GLM, RF, SVM, XGB) were used to construct prediction models for the key gene sets, and the models with the highest accuracy and stability were selected.Results Thirteen differentially expressed cuproptosis-related genes (FDX1, LIPT1, DLD, DLAT, PDHA1, PDHB, GLS, ATP7B, SLC31A1, ATP7A, DLST, DBT, GCSH) were screened. Based on these 13 genes, 138 COVID-19 patients were divided into two types (C1, C2). There were obvious differences in the infiltration of immune cells, expression of cuproptosis-related genes and gene pathway variation sets (GSVA) between different typing COVID-19 patients. Finally, 11 genes that were significantly correlated with COVID-19 patients and different typing were selected by WGCNA method. The prediction model constructed by SVM method with 5 key genes (RSAD2, AIM2, ZBP1, DDX60L, GBP1) had strong predictive ability (training group AUC = 0.956, validation group AUC = 0.931).Conclusion Multiple cuproptosis-related genes expressions are significantly correlated with COVID-19 subtypes (C1, C2) and the amount of immune cells infiltration; The prediction model constructed by SVM method can predict the probability of patient infection with COVID-19 well.
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COVID-19ABSTRACT
This retrospective study explored the changes in biomarkers indicators and prognosis in COVID-19 patients with mental disorders (n = 60) from the author’ Hospital between 2/13/2020 and 4/15/2020. Significant differences before and after negative conversion were observed in lymphocytes, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, aspartate aminotransferase, albumin, albumin/globulin ratio, direct bilirubin, alkaline phosphatase, uric acid, high-density lipoprotein cholesterol, and ApoA1 (all P < 0.05). Compared with the patients who had a negative conversion within 3 weeks, those who did not turn negative within 3 weeks had a higher frequency of cardiovascular diseases (27.3% vs. 4.2%, P = 0.040), a higher lymphocyte-to-monocyte ratio (median, 4.72 vs. 3.35, P = 0.003), and higher total bilirubin levels (median, 12.0 vs. 8.6 µmol/L, P = 0.031). The results present the changes in laboratory parameters in COVID-19 patients with a mental disorder. Cardiovascular diseases and higher lymphocyte-to-monocyte ratio, and total bilirubin levels could be associated with the amount of time required for negative conversion.
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Cardiovascular Diseases , Mental Disorders , Hyperbilirubinemia , COVID-19ABSTRACT
COVID-19 prognoses suggests that a proportion of patients develop fibrosis, but there is no evidence to indicate whether patients have progression of mesenchymal transition (MT) in the lungs. The role of MT during the COVID-19 pandemic remains poorly understood. Using single-cell RNA sequencing, we profiled the transcriptomes of cells from the lungs of healthy individuals (n = 45), COVID-19 patients (n = 58), and idiopathic pulmonary fibrosis (IPF) patients (n = 64) human lungs to map the entire MT change. This analysis enabled us to map all high-resolution matrix-producing cells and identify distinct subpopulations of endothelial cells (ECs) and epithelial cells as the primary cellular sources of MT clusters during COVID-19. For the first time, we have identied early and late subgroups of endothelial mesenchymal transition (EndMT) and epithelial-mesenchymal transition (EMT) using analysis of public databases for single-cell sequencing. We assessed epithelial subgroups by age, smoking status, and gender, and the data suggest that the proportional changes in EMT in COVID-19 are statistically significant. Further enumeration of early and late EMT suggests a correlation between invasive genes and COVID-19. Finally, EndMT is upregulated in COVID-19 patients and enriched for more inflammatory cytokines. Further, by classifying EndMT as early or late stages, we found that early EndMT was positively correlated with entry factors but this was not true for late EndMT. Exploring the MT state of may help to mitigate the fibrosis impact of SARS-CoV-2 infection.
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Objective: To assess the risk of public health emergencies, including both indigenous and imported ones, which might occur in the mainland of China in June 2022.
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Objection: The psychological health of thyroid cancer patients cannot be ignored, however, few studies have been conducted on the psychological status and influencing factors of thyroid cancer patients before radioactive iodine (RAI) therapy. The aim of the study was to investigate the incidence and risk factors of anxiety and depression in thyroid cancer patients prior to RAI therapy. Methods: : Clinical data were collected among differentiated thyroid cancer patients (DTCs) preparing for RAI therapy. Anxiety and depression were measured before RAI therapy by using the Generalized Anxiety Disorder Questionnaire (GAD-7) and Patient Health Questionnaire (PHQ-9). We used chi-square test and logistic regression analysis to find independent risk factors for anxiety and depression. Results: : A total of 112 patients with thyroid cancer were included. Of these, 72.32 % (n=81) were female, with an mean age of 41.50 years. Anxiety and depression were reported by 46 (41.08%) and 38 (33.93%) patients, respectively. Based on chi-square test and univariate logistic regression analysis, being female and having ever-experienced RAI therapy were independent risk factors for anxiety and depression among DTCs prior to RAI therapy. On multivariate analysis, only being female was significant associated with anxiety and depression in these patients. Conclusions: : Incidence of anxiety and depression increased in DTCs prior to RAI therapy. Being female and ever-experienced RAI therapy significant influenced anxiety and depression. Based on these findings, anxiety and depression assessment should be an important part of pre-RAI therapy in DTCs, and appropriate psychological nursing intervention can be carried out for key patients.
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Anxiety Disorders , Thyroid NeoplasmsABSTRACT
Smart classrooms are a relatively confined public space for college students. SARS-COV-2 and other respiratory viruses have been shown to pose a more significant threat to human health in relatively confined spaces. Using numerical simulation method to simulate the transmission and concentration distribution of virus-carrying droplets in smart classrooms in three different seasons (summer, winter, transitional seasons: spring and autumn). The Realizable k-ε model is used to simulate the airflow pattern in the smart classroom, and the Lagrangian method is used to simulate the transmission of droplets. The transmission process of droplets produced from the teacher standing on the platform and the student sitting on the seat is studied. The influence of three kinds of outdoor temperature on droplet transmission and the body deposition fraction of people in the smart classroom is analyzed. The results show that droplet transmission speed is maximum at the temperature of 5 degrees when the outdoor temperature is 5 °C, 20 °C, and 35 °C respectively. At 10 s, the transmission distance of droplets increases by 9.55% compared with that at 20 °C and 10.31% compared with that at 35 °C. In addition, the body deposition fraction is also affected by the location of the vent, with downwind contact being 6 times more likely than upwind contact. The research results can provide suggestions and measures for epidemic prevention and control in smart classrooms.
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Due to the COVID‐19 pandemic, many rapid antimicrobial agents have developed intensively. Carbon dots (CDs), a new type of carbon‐based nanomaterials, shows great potential against emerging infectious diseases and antimicrobial‐resistant infections due to their unique optical properties, excellent biocompatibility, and easy surface modification. With the definition of the CDs structure and properties, synthesis, and characteristic technology improvement, the research on the CDs as antimicrobial agents has made significant progress. However, the lack of high repeatable and exact preparation methods, and the regular antimicrobial activity make it far from practical application. In this review, we summarize the most recent progress and challenges of CDs antimicrobial. First, an overview of the characteristics and properties is given, and the advantage of CDs applied to antimicrobial is further discussed. Then, it focuses on research progress on antimicrobial mechanisms under different conditions, the critical factors affecting their antimicrobial activity, and the practical antimicrobial applications. Finally, the main challenges and future research perspectives of antimicrobial CDs are proposed.
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Acute lung injury (ALI) or its aggravated stage acute respiratory distress syndrome (ARDS) is a common severe clinical syndrome in intensive care unit, may lead to a life-threatening form of respiratory failure, resulting in high mortality up to 30–40% in most studies. Nanotechnology-mediated anti-inflammatory therapy is an emerging novel strategy for the treatment of ALI, has been demonstrated with unique advantages in solving the dilemma of ALI drug therapy. Artesunate (ART), a derivative of artemisinin, has been reported to have anti-inflammatory effects. Therefore, in the present study, we designed and synthesized PEGylated ART prodrugs and assessed whether ART prodrugs could attenuate lipopolysaccharide (LPS) induced ALI in vitro and in vivo. All treatment groups were conditioned with ART prodrugs 1 h before challenge with LPS. Significant increased inflammatory cytokines production and decreased GSH levels were observed in the LPS stimulated mouse macrophage cell line RAW264.7. Lung histopathological changes, lung W/D ratio, MPO activity and total neutrophil counts were increased in the LPS-induced murine model of ALI via nasal administration. However, these results can be reversed to some extent by treatment of ART prodrugs. The effectiveness of mPEG2k-SS-ART in inhibition of ALI induced by LPS was confirmed. In conclusion, our results demonstrated that the ART prodrugs could attenuate LPS-induced ALI effectively, and mPEG2k-SS-ART may serve as a novel strategy for treatment of inflammation induced lung injury.
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Background: Many people have experienced a high burden due to the spread of the coronavirus disease (COVID-19) and its serious consequences for health and everyday life. Prior studies have reported that physical activity (PA) may lower the risk of COVID-19 hospitalization. The present meta-analysis (PROSPERO registration number: CRD42022339672) explored the dose-response relationship between PA and the risk of COVID-19 hospitalization. Methods: Epidemiological observational studies on the relationship between PA and the risk of COVID-19 hospitalization were included. Categorical dose-response relationships between PA and the risk of COVID-19 hospitalization were assessed using random effect models. Robust error meta-regression models assessed the continuous relationship between PA (metabolic equivalent [MET]-h/week) and COVID-19 hospitalization risk across studies reporting quantitative PA estimates. Results: Seventeen observational studies (cohort\case-control\cross-section) met the criteria for inclusion in the meta-analysis. Categorical dose-relationship analysis showed a 40% (risk ratio (RR) 0.60, 95% confidence intervals (CI): 0.48-0.71) reduction in the risk of COVID-19 hospitalization compared to the lowest dose of PA. The results of the continuous dose-response relationship showed a non-linear inverse relationship (Pnon-linearity < 0.05) between PA and the risk of COVID-19 hospitalization. When total PA was less than or greater than 10 Met-h/week, an increase of 4 Met-h/week was associated with a 14% (RR = 0.83, 95%CI: 0.85-0.87) and 11% (RR = 0.89, 95%CI: 0.87-0.90) reduction in the risk of COVID-19 hospitalization, respectively. Conclusions: There was an inverse non-linear dose-response relationship between PA level and the risk of COVID-19 hospitalization. Doses of the guideline-recommended minimum PA levels by WTO may be required for more substantial reductions in the COVID-19 hospitalization risk.
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COVID-19 , Coronavirus InfectionsABSTRACT
To obtain the price return and price volatility spillovers between renewable energy stocks, technology stocks, oil futures and carbon allowances under different investment horizons, this paper employs a frequency-dependent method to study the dynamic connectedness between these assets in four frequency bands. The results show that, first, there is a strong spillover effect between these assets from a system-wide perspective, and it's mainly driven by short-term spillovers. Second, in the time domain, technology stocks have a more significant impact on renewable energy stocks compared to crude oil. However, through the study in the frequency domain, we find renewable energy stocks exhibit a more complex relationship with the other two assets at different time scales. Third, renewable energy stocks have significant spillover effect on carbon prices only in the short term. On longer time scales, other factors such as energy prices, climate and policy may have a greater impact on carbon allowance prices. Fourth, the spillover effect of the system is time-varying and frequency-varying. During the European debt crisis, the international oil price decline and the COVID-19 pandemic, the total spillover index of the system has experienced a substantial increase, mainly driven by medium, medium to long or long term spillovers.
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The low COVID-19 vaccination rate and intention among parents with children ages 5-11 is a seriousissue as the U.S. attempts to control the pandemic. According to current research, safety and side effectconcerns are the primary reason for such vaccine hesitancy. To mitigate it and boost vaccinationintention, we used two “safety” messages (a short, concise one vs. a long, detailed one) that carefullyexplained the vaccine’s lower dosage, the low rate of side effects, and the rigorous approval process.We also compared these safety messages with two “protect” messages which respectively emphasizedthe protection of the child and the entire family. Among the four treatment arms, we found that the shortversion of the safety message increased the average vaccination intention by over 1 point on a 0-6 scale,compared to the control arm, while the other three treatments failed to show significance. Specifically,these effects are particularly pronounced (~2 points) for Republican parents who had a much lowerinitial intention. Our study highlights the importance of to-the-point and concise information renderingin promoting public health activities and therefore has important policy implications for raisingvaccination intentions among parents, especially those leaning towards more conservative politicalaffiliation.
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COVID-19ABSTRACT
In closed buses, the spread of droplets with viruses/bacteria may cause the spread of respiratory infectious diseases. Discrete phase modeling is used to simulate the diffusion characteristics and concentration distribution of droplets at different temperatures and different exhalation positions by ANSYS FLUENT software. The integral concentration of droplets at different locations can be quantified, which leads to identification of low-risk areas and high-risk areas in the bus. Results show that a higher outdoor temperature leads to lower droplets’ diffusion speed and longer time until the droplets reach the driver. In addition, based on the integral concentration of droplets at the seats, regardless of whether a passenger exhales droplets in the front row of the bus, the position of the rear door or the last row of the bus, the seats in the last row of the bus away from the door belong to the low-risk area. In contrast, the seats near the door and the middle seat in the bus are higher risk areas. Consequently, this study proposed sitting on a seat in the low-risk area as a means to reduce the risk of passengers. Moreover, safety protection facilities around the driver should be modified to improve the isolation of the upper area of the driver’s location, so as to effectively prevent the droplet diffusion towards the driver, thereby effectively reducing the driver’s risk of infection.
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The spike (S), a homotrimer glycoprotein, is the most important antigen target in the research and development of SARS-CoV-2 vaccine. There is no doubt that fully simulating the advanced structure of this homotrimer in the subunit vaccine development strategy is the most likely way to improve the immune protective effect of the vaccine. In this study, the preparation strategies of S protein receptor-binding domain (RBD) trimer, S1 region trimer, and ectodomain (ECD) trimer nanoparticles were designed based on ferritin nanoparticle self-assembly technology. The Bombyx mori baculovirus expression system was used to prepare these three nanoparticle vaccines with high expression levels in the silkworm. The immune results of mice show that the nanoparticle vaccine prepared by this strategy can not only induce an immune response by subcutaneous administration but also effective by oral administration. Given the stability of these ferritin-based nanoparticles vaccine, easy-to-use and low-cost oral immunization strategy can make up for the vaccination blind areas caused by the shortage of ultralow-temperature equipment and medical resources in underdeveloped areas. And the oral vaccine is also a very potential candidate to cut off the spread of SARS-CoV-2 in domestic and farmed animals, especially in stray and wild animals.
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Under the threat of COVID-19, many regions around the world implemented lockdown policies to control the spread of the virus. This restriction on both social and economic activities has improved the quality of the environment in certain aspects. However, most previous studies have only focused on the short-term impact of lockdown policies on the urban environment. The long-term effects of lockdown require a more focused exploration and analysis. Thus, five major urban agglomerations in China were selected as the research area;changes in the numerical and spatial distribution of NO2 concentration and surface temperature during four different lockdown stages in 2019, 2020, and 2021 were investigated to analyze the long-term effects of lockdown policies on the urban environment. The results indicated that the impact of shorter lockdowns was short-term and unsustainable;the NO2 concentrations increased again with the resumption of production. Compared with air pollutants, thermal environmental problems are more complex. The effect of the lockdown policy was not reflected in the decrease in the area proportions of the high- and sub-high-temperature regions but rather in the spatial distribution of the high-temperature area, which was manifested as a fragmentation and dispersion of heat source patches. In addition to the severity of the lockdown, the impact of the lockdown policy was also closely related to the level of development and industrial structure of each city. Among the urban environments of the five agglomerations, the most affected were the Yangtze River Delta and Yangtze River Middle-Reach urban agglomerations, which had the largest decline in NO2 concentrations and the most notable fragmentation of heat source patches.
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Objective: To explore the mechanism of Hanshi Zufei Formula (HSZFF) treating coronavirus disease 2019 (COVID-19) by the data mining analysis of network pharmacology and the molecular docking.