The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors’ knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors’ knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

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BackgroundPersistent high-risk type Human papillomavirus (HPV) infection is recognized as a necessary cause of cervical cancer. This study aimed to compare the HPV prevalence and risk factors between women residing in Hong Kong (HK) and Guangzhou (GZ) region of China.Methodology/Principal FindingsA total of 1,570 and 1,369 women were recruited from HK and GZ, respectively. The cytology samples were collected and tested for HPV infection. The overall and type-specific HPV prevalence and the potential risk factors for acquisition of HPV infection were studied. Women with normal cytology in the GZ cohort had significantly higher HPV prevalence (10%) than those in the HK cohort (6.2%, p<0.001). The patterns of the age-specific HPV prevalence were also different between the two cohorts. In the HK cohort, women at the age of 20–29 years old had the highest prevalence and a second peak was observed in the age of ≥60 years old. In the GZ cohort, the highest HPV prevalence was also observed in 20–29 years old but declined as the age increased and a second peak was not seen. HPV16 and HPV52 were the most common high-risk types found in the HK and GZ cohorts, respectively. Age was the most consistently observed independent risk factor for HPV infection in the HK, while the number of sexual partners had association in the GZ cohort.Conclusions/SignificanceOur study provides the current status and the epidemiological characteristics of HPV prevalence in Southern Chinese women. The results strongly suggested that population education and the effective cervical cancer screening would be vital in the prevention of cervical cancer.

All the 5 common etiologies were detected in this study, with rotavirus and norovirus being the 2 leading agents. Mixed viral infections were common in outpatient children with acute diarrhea, and rotavirus seemed to play a major role in mixed infections.

Background: The coronavirus disease poses an unprecedented challenge to health and epidemic prevention system, especially the healthcare of patients with cancer. We sought to study the impact of COVID-19 on lung cancer patients in our center. Methods:We initiated a retrospectively study to analyze the impact of COVID-19 on lung cancer patients in our center, who were accepted for routine anticancer treatment before the epidemic and planned to return to hospital in January and February of 2020.Results: A total of 161 cases of lung cancer were included in the final analysis. As of April 15, 95 patients had delayed their return visit, and 47 cases were finally designated as having delayed admission during the epidemic and having to discontinue or delay their regular anticancer treatments. Of these 47 delayed patients, 33 were evaluated for tumor status using a computed tomography scan, 6 of these 33 cases (18.18%) were diagnosed as progressive disease (PD), and 5 cases did not return for visit.Conclusions: This is the first study investigating impact of COVID-19 on non-COVID-19 lung cancer patients during the pandemic. The study demonstrates the significant impact of the COVID-19 crisis on oncological care, indicating the need for appropriate change of treatment decisions and continued follow-up and psycho-oncological support during this pandemic.