Pathogenicity, tissue tropism and potential vertical transmission of SARSr-CoV-2 in Malayan pangolins.

Malayan pangolin SARS-CoV-2-related coronavirus (SARSr-CoV-2) is closely related to SARS-CoV-2. However, little is known about its pathogenicity in pangolins. Using CT scans we show that SARSr-CoV-2 positive Malayan pangolins are characterized by bilateral ground-glass opacities in lungs in a similar manner to COVID-19 patients. Histological examination and blood gas tests are indicative of dyspnea. SARSr-CoV-2 infected multiple organs in pangolins, with the lungs the major target, and histological expression data revealed that ACE2 and TMPRSS2 were co-expressed with viral RNA. Transcriptome analysis indicated that virus-positive pangolins were likely to have inadequate interferon responses, with relative greater cytokine and chemokine activity in the lung and spleen. Notably, both viral RNA and viral proteins were detected in three pangolin fetuses, providing initial evidence for vertical virus transmission. In sum, our study outlines the biological framework of SARSr-CoV-2 in pangolins, revealing striking similarities to COVID-19 in humans.

The longitudinal association between internet addiction and depressive and anxiety symptoms among Chinese adolescents before and during the COVID-19 pandemic

Background The COVID-19 pandemic and related prevention policies, such as home quarantine or online courses, could increase the risks of experiencing internet addiction and mental health problems among Chinese adolescents. There is a lack of longitudinal evidence to show the association between internet addiction symptoms and psychological consequences (e.g., depressive and anxiety symptoms). Objective This study aimed to explore the association between internet addiction and depressive and anxiety symptoms before and during the coronavirus disease 2019 (COVID-19) pandemic. Methods An effective sample of 7,958 Chinese adolescents was recruited for this two-wave longitudinal survey conducted over a six-month interval. All participants completed two-wave surveys before and during the COVID-19 pandemic. A longitudinal cross-lagged path model was used to analyze the associations between internet addiction and depressive and anxiety symptoms after controlling for four covariates (i.e., age, sex, minority, and COVID-19 influence). Results Higher depressive and anxiety symptoms before COVID-19 significantly predicted severe internet addiction during COVID-19. Results showed a significant bidirectional relationship between internet addiction and depressive symptoms. Furthermore, the prevalence of internet addiction displayed an increasing trend over the two waves. Conversely, a reduced prevalence of anxiety and depressive symptoms was observed over the two waves. Conclusion This current study provided valuable evidence that psychological problems and internet addiction significantly influenced each other before and during the COVID-19 outbreak. Consequently, the presence of psychological problems before and during the COVID-19 outbreak could indicate internet addiction. Thus, depression- and anxiety-related psychotherapies should be developed to prevent internet addiction among Chinese adolescents.

An ROS Architecture for Autonomous Mobile Robots with UCAR Platforms in Smart Restaurants

To lessen the spread of COVID-19 and other dangerous bacteria and viruses, contactless distribution of different items has gained widespread popularity. In order to complete delivery tasks at a catering facility, this paper explores the development of an autonomous mobile robot. The robot, in particular, plans its path and maintains smooth and flexible mobility using a Time Elastic Band (TEB) motion control method and an upgraded Dijkstra algorithm. On the open-source AI platform of iFLYTEK, a voice recognition module was trained to recognize voice signals of different tones and loudness, and an image recognition capability was attained using YOLOv4 and SIFT. The UCAR intelligent vehicle platform, made available by iFLYTEK, served as the foundation for the development of the mobile robot system. The robot took part in China's 16th National University Student Intelligent Car Race, an experimental demonstration test of the developed mobile robotics. The results of the experiments and task tests demonstrated that the proposed robot architecture was workable. In addition, we designed and put together a mobile robot utilizing components from the Taobao website. Compared to UCAR, this robot is less expensive and has the flexibility to be used in a variety of real-world settings.

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review (preprint)

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed.

Isolation of SARS-CoV-2-related coronavirus from Malayan pangolins.

The current outbreak of coronavirus disease-2019 (COVID-19) poses unprecedented challenges to global health1. The new coronavirus responsible for this outbreak-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-shares high sequence identity to SARS-CoV and a bat coronavirus, RaTG132. Although bats may be the reservoir host for a variety of coronaviruses3,4, it remains unknown whether SARS-CoV-2 has additional host species. Here we show that a coronavirus, which we name pangolin-CoV, isolated from a Malayan pangolin has 100%, 98.6%, 97.8% and 90.7% amino acid identity with SARS-CoV-2 in the E, M, N and S proteins, respectively. In particular, the receptor-binding domain of the S protein of pangolin-CoV is almost identical to that of SARS-CoV-2, with one difference in a noncritical amino acid. Our comparative genomic analysis suggests that SARS-CoV-2 may have originated in the recombination of a virus similar to pangolin-CoV with one similar to RaTG13. Pangolin-CoV was detected in 17 out of the 25 Malayan pangolins that we analysed. Infected pangolins showed clinical signs and histological changes, and circulating antibodies against pangolin-CoV reacted with the S protein of SARS-CoV-2. The isolation of a coronavirus from pangolins that is closely related to SARS-CoV-2 suggests that these animals have the potential to act as an intermediate host of SARS-CoV-2. This newly identified coronavirus from pangolins-the most-trafficked mammal in the illegal wildlife trade-could represent a future threat to public health if wildlife trade is not effectively controlled.

Point-of-care training program on COVID-19 infection prevention and control for pediatric healthcare workers (preprint)

Background: To evaluate the effectiveness of training on knowledge and practices of infection prevention and control (IPC) among pediatric health care workers (HCW) in Shanghai, China, in the context of COVID-19 pandemic. Methods: An online training program was designed by the Shanghai Pediatric Clinical Quality Control Center (SPQCC) during the early phase of COVID-19 pandemic on disease knowledge and practice of IPC. Training took place in the 81 partner hospitals affiliated with SPQCC. A 25-item self-administered questionnaire was used to evaluate knowledge gained from the training. Stratified-random sampling was used to select HCW according to three professionals (i.e., pediatricians, nurses and administrators) within each partner hospital. Awareness and knowledge of COVID-19 and its related infection control and practice was assessed by comparing survey results between different types of hospitals, professionals and professional ranks. A higher survey score meant that the respondent was more prepared and knowledgeable about COVID-19 and its infection control measures. Results: Completed questionnaires were returned from 1,062 subjects (385 pediatricians, 410 nurses, and 267 administrators), giving a response rate of 96.5%. Overall, awareness of clinical information related to COVID-19, importance of personal hygiene and isolation policy was high among the respondents. No statistical difference of scores on knowledge of COVID-19, IPC and relevant practice between the tertiary and peripheral hospitals. Among all respondents, middle-ranked health care personnel were most knowledgeable and achieved the highest score. Conclusions: Majority of pediatric HCW showed good recognition and practice in infection protection and control measures. The online training was able to achieve its aim to enhance knowledge and awareness and could have contributed to the zero infection rate among HCW caring for confirmed COVID-19 cases in Shanghai.

The impact of temperature and absolute humidity on the coronavirus disease 2019 (COVID-19) outbreak - evidence from China (preprint)

OBJECTIVE To investigate the impact of temperature and absolute humidity on the coronavirus disease 2019 (COVID-19) outbreak. DESIGN Ecological study. SETTING 31 provincial-level regions in mainland China. MAIN OUTCOME MEASURES Data on COVID-19 incidence and climate between Jan 20 and Feb 29, 2020. RESULTS The number of new confirm COVID-19 cases in mainland China peaked on Feb 1, 2020. COVID-19 daily incidence were lowest at -10 and highest at 10 ,while the maximum incidence was observed at the absolute humidity of approximately 7 g/m3. COVID-19 incidence changed with temperature as daily incidence decreased when the temperature rose. No significant association between COVID-19 incidence and absolute humidity was observed in distributed lag nonlinear models. Additionally, A modified susceptible-exposed-infectious-recovered (M-SEIR) model confirmed that transmission rate decreased with the increase of temperature, leading to further decrease of infection rate and outbreak scale. CONCLUSION Temperature is an environmental driver of the COVID-19 outbreak in China. Lower and higher temperatures might be positive to decrease the COVID-19 incidence. M-SEIR models help to better evaluate environmental and social impacts on COVID-19.