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In late December 2019, a cluster of atypical pneumonia cases was noticed in Wuhan, China, which was later identified as an outbreak of coronavirus disease 2019 (COVID‐19). Since then, it has spread globally, causing public health emergency and urgency, and declared as a pandemic by the World Health Organization (WHO). As of 1 April 2020, the data shows more than 200 countries and territories have been affected, with more than 824,000 confirmed cases and 41,000 deaths. Across the globe, many cancer patients visit the hospital and clinics for treatment and investigations. A large number of this population are immunocompromised, either due to their underlying disease or cancer treatments which put them at higher risk for infection and complications. In addition, sev-eral risk factors have been identified that increase the risk and severity of infection with COVID-19, and cancer patients commonly have many of them. Recently, a large retrospective study among cancer patients infected with COVID-19 in Wuhan, China, found a higher incidence of severe events in cancer patients compared to patients without cancer. As doctors working amid this pandemic, we all have responsibilities and duties to act upon local guidelines to ensure the continuation of essential cancer services without overwhelming the health care system. In this review, we addressed the po-tential challenges and possible actions for clinicians to manage cancer patients during this unusual time. © 2022, An-Najah National University. All rights reserved.
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Background: Understanding the profile of antibody responses following acute COVID-19 infection is required. Aim: to describe the pattern of IgG anti-COVID-19 antibody production in patients with acute infection using the LABScreen COVID Plus assay. Results: The overall seropositivity was 69/73(94.5%). Anti-Spike, Spike 1 and spike S2 subunits were positive in 78.1%, while anti spike receptor binding domain (RBD) was detected in 68.4% and anti nucleocapsid protein in 61.6%. The overall positivity of the assay reached 100.0% during the second week post symptoms. The mean fluorescent intensities (MFI) of anti-Spike S1 was higher in the second week than the first week, p=0.03. MFI of anti-Spike S2 was significantly higher in PCR positive patients in comparison with the negative ones, p=0.006. When compared to the RT-PCR results;the overall antibodies positivity, anti-Spike, and anti-Spike2 antibodies had sensitivities (100% and 84.7%) and specificities (28.6% and 50.0%) and accuracies (86.3% and 78.1%). Patients' outcome correlated significantly with the time of hospital admission, p=0.001. Conclusion: COVID-19 IgG antibodies are detectable with considerable frequencies during the first two weeks post infection. Anti S2 antibodies correlates well with the RT-PCR results. The LABScreen COVID Plus is a sensitive assay for the detection of post-acute COVID-19 infection antibody responses. © 2020 The author (s).
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OBJECTIVE: To present an established practice protocol for safe and effective hospital-setting ophthalmic practice during the coronavirus disease 2019 (COVID-19) pandemic. METHODS AND ANALYSIS: Literature was reviewed to identify articles relevant to COVID-19 pandemic and ophthalmology. The following keywords were used: COVID-19, SARS-CoV-2 and telemedicine, combined with eye, ophthalmology, conjunctivitis and tears. Data were extracted from the identified manuscripts and discussed among subspecialists to obtain consensus evidence-based practice. RESULT(S): A protocol for ophthalmic practice in the era of COVID-19 pandemic was established. The protocol covered patient screening, clinic flow, required personal protective equipment and modifications of ophthalmic equipment for improved safety. CONCLUSION(S): Important literature emerged with respect to the practice of ophthalmology in the era of COVID-19. An evidence-based ophthalmic practice protocol was established and should be modified in the future to accommodate new insights on the COVID-19 pandemic.Copyright © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
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The outbreak of the COVID-19 global pandemic has affected millions and has a severe impact on our daily lives. To support radiologists in this overwhelming challenge, we developed a weakly supervised deep learning framework that can detect, localize, and quantify the severity of COVID-19 disease from chest CT scans using limited annotations. The framework is designed to rapidly provide a solution during the initial outbreak of a pandemic when datasets availability is limited. It is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification, and fine-grained localization. In addition, we present the Coronascore bio-marker which corresponds to the severity grade of the disease. Finally, we present an unsupervised feature space clustering which can assist in understanding the COVID-19 radiographic manifestations. We present our results on an external dataset comprised of 199 patients from Zhejiang province, China. © 2020, Springer Nature Switzerland AG.