Coronavirus disease 2019 (COVID-19) is an emerging infectious disease first identified in Wuhan City, Hubei Province, China. As of 19 February 2020, there had been 333 confirmed cases reported in Shanghai, China. This study elaborates on the epidemiological and clinical characteristics of COVID-19 based on a descriptive study of the 333 patients infected with COVID-19 in Shanghai for the purpose of probing into this new disease and providing reference. Among the 333 confirmed cases in Shanghai, 172 (51.7%) were males and 161 (48.3%) were females, with a median age of 50 years. 299 (89.8%) cases presented mild symptoms. 139 (41.7%) and 111 (33.3%) cases were infected in Wuhan and Shanghai, respectively. 148 (44.4%) cases once had contact with confirmed cases before onset, while 103 (30.9%) cases had never contacted confirmed cases but they had a sojourn history in Wuhan. The onset date of the first case in Shanghai was 28 December, with the peak appearing on 27 January. The median incubation period of COVID-19 was estimated to be 7.2 days. 207 (62.2%) cases had fever symptoms at the onset, whereas 273 (82.0%) cases experienced fever before hospitalization. 56 (18.6%) adults experienced a decrease in white blood cell and 84 (42.9%) had increased C-reactive protein after onset. Elderly, male and heart disease history were risk factors for severe or critical pneumonia. These findings suggest that most cases experienced fever symptoms and had mild pneumonia. Strengthening the health management of elderly men, especially those with underlying diseases, may help reduce the incidence of severe and critical pneumonia. Time intervals from onset to visit, hospitalization and diagnosis confirmed were all shortened after Shanghai's first-level public health emergency response. Shanghai's experience proves that COVID-19 can be controlled well in megacities.

The coronavirus disease (COVID-19) pandemic is a major challenge worldwide.However, the epidemic potential of common human coronaviruses (HCoVs) remains unclear. This study aimed to determine the epidemiological and coinfection characteristics of common HCoVs in individuals with influenza-like illness (ILI) and severe acute respiratory infection (SARI). This retrospective, observational, multicentre study used data collected from patients admitted to nine sentinel hospitals with ILI and SARI from January 2015 through December 2020 in Shanghai, China. We prospectively tested patients for a total of 22 respiratory pathogens using multi-real-time polymerase chain reaction. Of the 4541 patients tested, 40.37% (1833/4541) tested positive for respiratory pathogens and 3.59% (163/4541) tested positive for common HCoVs. HCoV infection was more common in the non-endemic season for respiratory pathogens (odds ratio: 2.33, 95% confidence interval: 1.64-3.31). HCoV-OC43 (41.72%, 68/163) was the most common type of HCoV detected. The co-infection rate was 31.29% (51/163) among 163 HCoV-positive cases, with HCoV-229E (53.13%, 17/32), the HCoV type that was most frequently associated with co-infection.Respiratory pathogens responsible for co-infections with HCoVs included parainfluenza virus, rhinovirus/enterovirus, influenza A virus, and adenovirus. Furthermore, we identified one patient co-infected with HCoV-OC43 and HCoV-NL63/HKU1. The prevalence of common HCoVs remains low in ILI/SARI cases, in Shanghai. However, the seasonal pattern of HCoVs may be opposite to that of other respiratory pathogens. Moreover, HCoVs are likely to co-exist with specific respiratory pathogens. The potential role of co-infections with HCoVs and other pathogenic microorganisms in infection and pathogenesis of ILI and SARI warrants further study.

BackgroundChronic hepatitis B (CHB) infection during pregnancy is associated with insulin resistance. A meta-analytic technique was used to quantify the evidence of an association between CHB infection and the risk of gestational diabetes (GDM) among pregnant women.MethodsWe searched PubMed for studies up to September 5th 2013. Additional studies were obtained from other sources. We selected studies using a cohort-study design and reported a quantitative association between CHB infection during pregnancy and risk of GDM. A total of 280 articles were identified, of which fourteen publications involving 439,514 subjects met the inclusion criteria. A sequential algorithm was used to reduce between-study heterogeneity, and further meta-analysis was conducted using a random-effects model.ResultsTen out of the fourteen studies were highly homogeneous, indicating an association of 1.11 [the adjusted odds ratio, 95% confidence interval 0.96 - 1.28] between CHB infection during pregnancy and the risk of developing GDM. The heterogeneity of the additional four studies may be due to selection bias or possible aetiological differences for special subsets of pregnant women.ConclusionsThese results indicate that CHB infection during pregnancy is not associated with an increased risk of developing GDM among pregnant women except those from Iran.

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Underwater Acoustic Sensor Networks (UASNs) are an important technical means to explore the ocean realm. However, most UASNs rely on hardware infrastructures with poor flexibility and versatility. The systems typically deploy in a redundant manner, which not only leads to waste but also causes serious signal interference due to multiple noises in designated underwater regions. Software-Defined Networking (SDN) is a novel network paradigm, which provides an innovative approach to improve flexibility and reduce development risks greatly. Although SDN and UASNs are hot topics, there are currently few studies built on both. In this paper, we provide a comprehensive review on the advances in software-defined UASNs. First, we briefly present the background, and then we review the progress of the Software-Defined Radio (SDR), Cognitive Radio (CR), and SDN. Next, we introduce the current issues and potential research areas. Finally, we conclude the paper and present discussions. Based on this work, we hope to inspire more active studies and take a further step on software-defined UASNs with high performances.