Behaviors in Educational Settings during and after the COVID-19 Pandemic.

This editorial is an introduction to the Special Issue "Behaviors in Educational Setting" [...].

Air Pollution Increased the Demand for Gym Sports under COVID-19: Evidence from Beijing, China.

Air pollution may change people's gym sports behavior. To test this claim, first, we used big data crawler technology and ordinary least square (OLS) models to investigate the effect of air pollution on people' gym visits in Beijing, China, especially under the COVID-19 pandemic of 2019-2020, and the results showed that a one-standard-deviation increase in PM2.5 concentration (fine particulate matter with diameters equal to or smaller than 2.5 µm) derived from the land use regression model (LUR) was positively associated with a 0.119 and a 0.171 standard-deviation increase in gym visits without or with consideration of the COVID-19 variable, respectively. Second, using spatial autocorrelation analysis and a series of spatial econometric models, we provided consistent evidence that the gym industry of Beijing had a strong spatial dependence, and PM2.5 and its spatial spillover effect had a positive impact on the demand for gym sports. Such a phenomenon offers us a new perspective that gym sports can be developed into an essential activity for the public due to this avoidance behavior regarding COVID-19 virus contact and pollution exposure.

Current Research Trends in Cytokine Storm: A Scientometric Study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is currently rampant worldwide, resulting in unpredictable harm to humans. High blood levels of cytokines and chemokines have been marked in patients with COVID-19 infection, leading to cytokine storm syndrome. Cytokine storms are violent inflammatory immune responses that reveal the devastating effect of immune dysregulation and the critical role of an effective host immune response. METHODS: Scientometric analysis summarizes the literature on cytokine storms in recent decades and provides a valuable and timely approach to tracking the development of new trends. This review summarizes the pathogenesis and treatment of diseases associated with cytokine storms comprehensively based on scientometric analysis. RESULTS: Field distribution, knowledge structure, and research topic evolution correlated with cytokine storms are revealed, and the occurrence, development, and treatment of disease relevant to cytokine storms are illustrated. CONCLUSION: Cytokine storms can be induced by pathogens and iatrogenic causes and can also occur in the context of autoimmune diseases and monogenic diseases as well. These reveal the multidisciplinary nature of cytokine storms and remind the complexity of the pathophysiological features, clinical presentation, and management. Overall, this scientometric study provides a macroscopic presentation and further direction for researchers who focus on cytokine storms.

Association of Use of Tourniquets During Total Knee Arthroplasty in the Elderly Patients With Post-operative Pain and Return to Function.

Objective: During total knee arthroplasty (TKA), tourniquet may negatively impact post-operative functional recovery. This study aimed at investigating the effects of tourniquet on pain and return to function. Methods: Pubmed, Embase, and Cochrane Library were comprehensively searched for randomized controlled trials (RCTs) published up to February 15th, 2020. Search terms included; total knee arthroplasty, tourniquet, and randomized controlled trial. RCTs evaluating the efficacies of tourniquet during and after operation were selected. Two reviewers independently extracted the data. Effect estimates with 95% CIs were pooled using the random-effects model. Dichotomous data were calculated as relative risks (RR) with 95% confidence intervals (CI). Mean differences (MD) with 95% CI were used to measure the impact of consecutive results. Primary outcomes were the range of motion (ROM) and visual analog scale (VAS) pain scores. Results: Thirty-three RCTs involving a total of 2,393 patients were included in this study. The mean age is 65.58 years old. Compared to no tourniquet group, the use of a tourniquet resulted in suppressed ROM on the 3rd post-operative day [MD, -4.67; (95% CI, -8.00 to -1.35)] and the 1st post-operative month [MD, -3.18; (95% CI, -5.92 to -0.44)]. Pain increased significantly when using tourniquets on the third day after surgery [MD, 0.39; (95% CI, -0.19 to 0.59)]. Moreover, tourniquets can reduce intra-operative blood loss [MD, -127.67; (95% CI, -186.83 to -68.50)], shorter operation time [MD, -3.73; (95% CI, -5.98 to -1.48)], lower transfusion rate [RR, 0.85; (95% CI, 0.73-1.00)], higher superficial wound infection rates RR, 2.43; [(5% CI, 1.04-5.67)] and higher all complication rates [RR, 1.98; (95% CI, 1.22-3.22)]. Conclusion: Moderate certainty evidence shows that the use of a tourniquet was associated with an increased risk of higher superficial wound infection rates and all complication rates. Therefore, the findings did not support the routine use of a tourniquet during TKA.

UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.

BACKGROUND: With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning. METHODS: This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality. RESULTS: The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models. CONCLUSION: In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.

Will individuals visit hospitals when suffering heat-related illnesses? Yes, but…

Under heat problem, the combined effects of heatwaves and urban heat island effects, has been one of the deadliest climate-related disasters. Uncovering heat-induced health problems is of significance to inform people about urban heat impacts and improve people's awareness of addressing urban heat problems. Existing studies have primarily done this through panel analysis based on second-hand data from local or national authorities. However, there are limited studies directly concentrating on the heat responses of people. To address this gap, this study aims to investigate public responses to urban heat and heat-related illness on the individual side. The study was conducted through a questionnaire survey in three Chinese cities including Nanchang, Shenyang and Xi'an. Based on 1154 valid responses, this study analysed respondents understanding of urban heat problems, symptoms of physiological illnesses and their behaviours of hospitalisation. The results indicate that the knowledge of heat-related risks (2.29 out of 5) was significantly lower than the perceived urban heat severity (3.24) and the perceived severity of physiological impacts (2.40). The skin heat damage (44.7%), among 873 respondents who underwent physiological impacts, was the most frequent physiological illness, followed by the digestive systems (34.0%) and then respiratory (24.1%) and cardiovascular diseases (18.2%). Among the 873 respondents, only 4.0% and 17.7% of respondents would like or were mostly yes to visit hospitals, while 14.2% and 26.4% of the respondents would not like or were mostly not to visit hospitals. Moreover, perceived urban heat severity, knowledge of heat-related risks, perceived severity of physiological impacts, symptoms of physiological illnesses and behaviours of hospitalisation were city-specific and demography-dependent. Overall, the empirical analysis provides new evidence of urban heat problems and generates theoretical and policy implications for heat-induced impact estimation and prevention.

Immunoreactive peptide maps of SARS-CoV-2.

Serodiagnosis of SARS-CoV-2 infection is impeded by immunological cross-reactivity among the human coronaviruses (HCoVs): SARS-CoV-2, SARS-CoV-1, MERS-CoV, OC43, 229E, HKU1, and NL63. Here we report the identification of humoral immune responses to SARS-CoV-2 peptides that may enable discrimination between exposure to SARS-CoV-2 and other HCoVs. We used a high-density peptide microarray and plasma samples collected at two time points from 50 subjects with SARS-CoV-2 infection confirmed by qPCR, samples collected in 2004-2005 from 11 subjects with IgG antibodies to SARS-CoV-1, 11 subjects with IgG antibodies to other seasonal human coronaviruses (HCoV), and 10 healthy human subjects. Through statistical modeling with linear regression and multidimensional scaling we identified specific peptides that were reassembled to identify 29 linear SARS-CoV-2 epitopes that were immunoreactive with plasma from individuals who had asymptomatic, mild or severe SARS-CoV-2 infections. Larger studies will be required to determine whether these peptides may be useful in serodiagnostics.

Early predictive factors of progression from severe type to critical ill type in patients with Coronavirus Disease 2019: A retrospective cohort study.

BACKGROUND: The current worldwide pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health, and the mortality rate of critical ill patients remains high. The purpose of this study was to identify factors that early predict the progression of COVID-19 from severe to critical illness. METHODS: This retrospective cohort study included adult patients with severe or critical ill COVID-19 who were consecutively admitted to the Zhongfaxincheng campus of Tongji Hospital (Wuhan, China) from February 8 to 18, 2020. Baseline variables, data at hospital admission and during hospital stay, as well as clinical outcomes were collected from electronic medical records system. The primary endpoint was the development of critical illness. A multivariable logistic regression model was used to identify independent factors that were associated with the progression from severe to critical illness. RESULTS: A total of 138 patients were included in the analysis; of them 119 were diagnosed as severe cases and 16 as critical ill cases at hospital admission. During hospital stay, 19 more severe cases progressed to critical illness. For all enrolled patients, longer duration from diagnosis to admission (odds ratio [OR] 1.108, 95% CI 1.022-1.202; P = 0.013), pulse oxygen saturation at admission <93% (OR 5.775, 95% CI 1.257-26.535; P = 0.024), higher neutrophil count (OR 1.495, 95% CI 1.177-1.899; P = 0.001) and higher creatine kinase-MB level at admission (OR 2.449, 95% CI 1.089-5.511; P = 0.030) were associated with a higher risk, whereas higher lymphocyte count at admission (OR 0.149, 95% CI 0.026-0.852; P = 0.032) was associated with a lower risk of critical illness development. For the subgroup of severe cases at hospital admission, the above factors except creatine kinase-MB level were also found to have similar correlation with critical illness development. CONCLUSIONS: Higher neutrophil count and lower lymphocyte count at admission were early independent predictors of progression to critical illness in severe COVID-19 patients.

Early predictive factors of progression from severe type to critical ill type in patients with Coronavirus Disease 2019: A retrospective cohort study (preprint)

Background: The current worldwide pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health, and the mortality rate of critical ill patients remains high. The purpose of this study was to identify factors that early predict the progression of COVID-19 from severe to critical illness.Methods: This retrospective cohort study included adult patients with severe or critical ill COVID-19 who were consecutively admitted to the Zhongfaxincheng campus of Tongji Hospital (Wuhan, China) from February 8 to 18, 2020. Baseline variables, data at hospital admission and during hospital stay, as well as clinical outcomes were collected from electronic medical records system. The primary endpoint was the development of critical illness. A multivariable logistic regression model was used to identify independent factors that were associated with the progression from severe to critical illness.Results: A total of 138 patients were included in the analysis; of them 119 were diagnosed as severe cases and 16 as critical ill cases at hospital admission. During hospital stay, 19 more severe cases progressed to critical illness. For all enrolled patients, longer duration from diagnosis to admission (odds ratio [OR] 1.108, 95% CI 1.022-1.202; P=0.013), pulse oxygen saturation at admission <93% (OR 5.775, 95% CI 1.257-26.535; P=0.024), higher neutrophil count (OR 1.495, 95% CI 1.177-1.899; P=0.001) and higher creatine kinase-MB level at admission (OR 2.449, 95% CI 1.089-5.511; P=0.030) were associated with a higher risk, whereas higher lymphocyte count at admission (OR 0.149, 95% CI 0.026-0.852; P=0.032) was associated with a lower risk of critical illness development. For the subgroup of severe cases at hospital admission, the above factors except creatine kinase-MB level were also found to have similar correlation with critical illness development.Conclusions: Higher neutrophil count and lower lymphocyte count at admission were early independent predictors of progression to critical illness in severe COVID-19 patients.