ABSTRACT
PURPOSE: The coronavirus 2019 (COVID-19) pandemic-caused by a new type of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-has posed severe impacts on public health worldwide and has resulted in a total of > 6 million deaths. Notably, male patients developed more complications and had mortality rates ~ 77% higher than those of female patients. The extensive expression of the SARS-CoV-2 receptor and related proteins in the male reproductive tract and the association of serum testosterone levels with viral entry and infection have brought attention to COVID-19's effects on male fertility. METHODS: The peer-reviewed articles and reviews were obtained by searching for the keywords SARS-CoV-2, COVID-19, endocrine, spermatogenesis, epididymis, prostate, and vaccine in the databases of PubMed, Web of Science and Google Scholar from 2020-2022. RESULTS: This review summarizes the effects of COVID-19 on the male reproductive system and investigates the impact of various types of SARS-CoV-2 vaccines on male reproductive health. We also present the underlying mechanisms by which SARS-CoV-2 affects male reproduction and discuss the potentially harmful effects of asymptomatic infections, as well as the long-term impact of COVID-19 on male reproductive health. CONCLUSION: COVID-19 disrupted the HPG axis, which had negative impacts on spermatogenesis and the epididymis, albeit further investigations need to be performed. The development of vaccines against various SARS-CoV-2 variations is important to lower infection rates and long-term COVID risks.
Subject(s)
COVID-19 , Infertility, Male , Humans , Male , Female , SARS-CoV-2 , COVID-19 Vaccines , Infertility, Male/etiology , SpermatogenesisABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic has caused a reduction in business and routine activity and resulted in less motor fuel consumption. Thus, the gas tax revenue is reduced, which is the major funding resource supporting the rehabilitation and maintenance of transportation infrastructure systems. The focus of this study is to evaluate the impact of the COVID-19 pandemic on transportation infrastructure funds in the United States through analyzing the motor fuel consumption data. Machine learning models were developed by integrating COVID-19 scenarios, fuel consumptions, and demographic data. The best model achieves an R2-score of more than 95% and captures the fluctuations of fuel consumption during the pandemic. Using the developed model, we project future motor gas consumption for each state. For some states, the gas tax revenues are going to be 10%-15% lower than the pre-pandemic level for at least one or two years. © 2022 International Conference on Transportation and Development
ABSTRACT
With the increasing extreme weather events and various disasters, people are paying more attention to environmental issues than ever, particularly global warming. Public debate on it has grown on various platforms, including newspapers and social media. This paper examines the topics and sentiments of the discussion of global warming on Twitter over a span of 18 months using two big data analytics techniques: topic modelling and sentiment analysis. There are seven main topics concerning global warming frequently debated on Twitter: factors causing global warming, consequences of global warming, actions necessary to stop global warming, relations between global warming and COVID-19, global warming's relation with politics, global warming as a hoax, and global warming as a reality. The sentiment analysis shows that most people express positive emotions about global warming, though the most evoked emotion found across the data is fear, followed by trust. The study provides a general and critical view of the public's principal concerns and their feelings about global warming on Twitter.
ABSTRACT
Introduction: This study is the first to focus on the different respiratory support modes and outcomes of critically ill patients with COVID- 19 in Wuhan. The cohort study design is persuasive. The number of studies retrieved is limited in patients with MDRO coinfected with COVID-19. This study only selected critically ill patients with COVID-19 in Leishenshan Hospital. Objectives: We aimed to describe the clinical characteristics and outcomes of five different modes of respiratory support among critically ill patients with coronavirus disease 2019 (COVID-19). Methods: This was a hospital-based, retrospective cohort study which setting on Leishenshan hospital in Wuhan, central China. Patients with COVID-19 admitted to the ICU of Leishenshan Hospital from February 8, 2020 to April 18, 2020 were recruited. The outcome was living status and survival time. Results: Thirty-five patients died among 114 hospitalization patients (mortality rate, 30.7%), and 56 patients (49.12%) used mechanical ventilation. The mean survival time (days) of patients without respiratory support, noninvasive positive-pressure ventilation (NIPPV), endotracheal intubation, tracheotomy, or endotracheal intubation before and after tracheotomy (EI + T) was 15, 34, 32, 12.5, and 6, respectively (p < 0.000). Eighteen (15.79%) patients were co-infected with MDROs, primarily in the EI + T group (83.33%). The mortality risk of patients treated with NIPPV and EI + T was 0.20 and 0.21 times higher than that of patients without any respiratory support (95% confidence interval [CI] = 0.002-0.203;95% CI = 0.002-0.218). The mode of respiratory support was an independent factor affecting the survival of ICU patients with COVID-19. Conclusion: Mortality risk in patients with NIPPV and EI + T was lower than in those without any respiratory support. Timely and correct respiratory support mode is the key to reducing the death of critically ill patients with COVID-19.
ABSTRACT
Using a carefully selected industry classification standard, we divided 102 industry security indices in China’s stock market into four demand-oriented sector groups and identified demand-oriented industry-specific volatility spillover networks. The demand-oriented concept is a new way in which to reconstruct the structure of the networks. Analyzing networks from a demand-oriented perspective can improve the understanding of the change in economic demand, especially when the macroeconomy is dramatically influenced by exogenous shocks, such as those due to the outbreak of COVID-19. At the beginning of the outbreak, spillover effects from industry indices of sectors meeting the investment demand to those meeting the consumption demands rose significantly in China's stock market. However, these spillover effects declined after the outbreak containment in China appeared to be effective. In addition, some service sectors, including utility, transportation and information services, have played increasingly important roles in the networks of industry-specific volatility spillovers since the COVID-19 outbreak. The efforts to contain the outbreak, led by the Chinese government, have been successful and work resumption has been organized with high efficiency. First, the risk of investment demand has therefore been controlled and eliminated relatively quickly. Second, the intensive use of non-pharmaceutical interventions (NPIs) has led to supply restrictions in services in China, which will still be a potential threat to economic recovery in the next stage. © 2020 AESS Publications. All Rights Reserved.