A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.

The effect of major public emergencies on technology-based SMEs: Survival crisis and relief paths

This paper explores the impact of major unexpected public event on the survivability of technology-based small and medium-sized enterprises and the role of external support policies and internal adjustment strategies in mitigating the shock. Using data on Growth Enterprise Market listed companies for 2018–2021, we found that COVID-19 outbreaks caused severe survival pressure on technology-based SMEs, which we see mainly in the reduction of development capacity, operation capacity, profitability, and debt servicing capacity. Additionally, we find that both external support policies and internal adjustment strategies can effectively mitigate the negative impact of COVID-19 on the viability of technology-based SMEs. © 2023 Elsevier Inc.

Associations between severe pulmonary function and residual CT abnormalities in rehabilitating COVID-19 patients

OBJECTIVE: Coronavirus disease 2019 (COVID-19) spread around the world in 2020. Abnormal pulmonary function and residual CT abnormalities were observed in COVID-19 patients during recovery. Appropriate rehabilitation training is around the corner. The correlation between spirometric impairment and residual CT abnormality remains largely unknown. PATIENTS AND METHODS: A cross-sectional study conducted on the pulmonary function of 101 convalescent COVID-19 patients before discharge. Multivariate analysis was used to establish a scoring system to evaluate the spirometric abnormality based on residual chest CT. RESULTS: Lung consolidation area >25% and severe-type COVID-19 were two independent risk factors for severe pulmonary dysfunction. Besides, a scoring system was established. People scoring more than 12 points have more chances (17 times) to get severe pulmonary function impairment before discharge. CONCLUSIONS: For the first time, a chest CT characteristics-based grading system was suggested to predict the pulmonary dysfunction of COVID-19 patients during convalescence in this study. This study may provide suggestions for pulmonary rehabilitation.

Face-to-face classes hijacked by COVID-19: What and how HEI instructors want to learn for online teaching

Upon the forced interruption of face-to-face classes because of COVID-19, HEIs moved online to deliver courses in the spring semester of the 2019-2020 academic year. A survey of 108 instructors from HEIs in Wuhan, Hubei, China was carried out to Figure out the situation or perception of HEI instructors before, during and after the online transition. The research reveals a severe lack of online teaching experience among teaching staff prior to the transition but they got prepared mainly by self-directed learning or attending training programs. In the pandemic semester, about two thirds of respondents used five-streamed delivery while the rest employed asynchronous teaching and the most-frequently-encountered problems were internet connection failure, fake attendance and poor involvement of students. Almost all respondents agreed upon an investment in and speed up of the development of online teaching resources in the post-pandemic period and their primary concerns for training lied in how to engage students, how to design in-class activities and online course assessment. As for forms of training, smaller and more focused programs like small workshops or short training videos were more favored by the respondents. The research also reveals some interesting differences between different categories of respondents. © 2020 IEEE.

COVID-19 Pandemic: A Usability Study on Platforms to Support eLearning

With the COVID-19 pandemic, the higher education communities throughout almost the entire world have moved from traditional face-to-face teaching to remote learning by using video conferencing software and online learning applications and platforms. With social distancing requirements, it is expected that eLearning will be part of the delivery modalities at least until an effective vaccine is widely available. Even after the pandemic is over, it is expected that remote learning and online education will be part of the “new or next normal.” Such online and remote learning modalities are not simply restricted to academic institutions. Businesses are using online and remote learning to re-train, re-tool, and re-educate their employees. The students, in general, are not enthusiastic about the virtual classroom. Niche.com surveyed 14,000 undergraduate and graduate students in April 2020 and found that more than 2/3 of them thought online classes are not as effective as in-person and teacher-centered classes. This unplanned change in teaching modes caused by COVID-19 and the negative feedback from students creates some serious concerns for educators and universities. How to enhance the eLearning experience for students? How to choose from many eLearning platforms on the market? Which eLearning platform is the most user friendly and the best suited for online classes? Which eLearning platforms enable the best class participation and student involvement? In this research, we apply the eLearning usability heuristics to evaluate the major video conferencing platforms (e.g., Cisco Webex, Microsoft Teams, and Zoom). © 2020, Springer Nature Switzerland AG.

Guidance on the Control and Prevention of SARS-CoV-2 Infection in Primary Healthcare Settings in Rural China(First Edition)

Since the outbreak of the COVID-19, all regions of China have carried out prompt responsive actions in an active way to control and prevent the disease, among which rural areas face particularly severe challenges, because they are the weak link in the control and prevention of public health emergencies due to shortage of health resources and poor diagnostic and therapeutic level. According to characteristics and the actual situation of the current rural primary healthcare settings, this guidedance introduced the requirements of setting environmental space for epidemic prevention and control, the process of referral, home observation, persons with special health care, health education for villagers, personal protection for health staff, and nosocomial infection control. It would provide a standardized and safe way for primary healthcare settings in rural areas in the epidemic prevention and control. Copyright © 2020 by the Chinese General Practice.