ABSTRACT
Background: The aim of the work was to evaluate the ocular manifestations in the patients with COVID-19 and its role in the prediction of the course and the outcome of the disease. Methods: This retrospective study was conducted at two tertiary referral COVID-19 isolation hospitals in two major university hospitals in Egypt. Two hundred and twenty-eight patients were enrolled in the study. The medical records of patients who had clinically confirmed COVID-19 between 1/5/2020 to 15/7/2020 were retrospectively reviewed. Data were collected from patient charts, including age, sex, accommodation, ocular manifestations, fever, headache, cough, dyspnea, anosmia, cyanosis, abdominal pain, anorexia, liver, kidney, cardiac manifestations, CT, X-ray finding, blood tests, and outcome of the disease. Results: Thirty-four patients with ocular manifestation were finally enrolled in the study with a mean age of 42.1 years;20 patients (58.8%) were men. The incidence of ocular manifestation was 14.9% (34/228). All patients with ocular manifestations had conjunctivitis (redness, epiphora, foreign body sensation), which had been treated and resolved completely within 10 days in all patients without any permanent ocular damage. There was a trend between the presence of ocular manifestations and the associated milder disease course, although this trend was not statistically significant. Conclusion: dOphthalmic manifestation is common in patients with COVID-19 and it occurs more frequently in patients with mild to moderate form COVID-19, but it could not predict the patient's mortality.
ABSTRACT
The development of 5G and artificial intelligence technologies have brought novel ideas to the prevention, control, and diagnosis of disease. Due to the limitation of the privacy protection of medical big data, releasing the data of patients is not allowed. However, as COVID-19 spreads globally, it is urgent to develop a robust diagnostic model to serve as many institutions as possible. Therefore, we propose a 5G-enabled architecture of auxiliary diagnosis based on federated learning for multiple institutions and central cloud collaboration to realize the sharing of diagnosis models with high generalization performance. In order to exchange model and parameters between the central and distributed nodes, a framework of diagnosis model cognition is constructed for sharing and updating the model adaptively. The severity classification experiments of COVID-19 were carried out on the central cloud and three edge cloud servers to verify the effectiveness of the proposed architecture and model cognition strategy. At the same time, the aggregated model shows good performance with accuracy rates of 95.3, 79.4, and 97.7 percent on distributed nodes, and the recognition results can assist doctors in executing auxiliary diagnosis. Finally, the open issues of model fusion of multimodal data in the 5G network architecture are discussed.
ABSTRACT
New corona virus disease COVID-19 is a pandemic outbreak viral infection that is highly contagious. The disease can affect any age groups. Majority of patients show mild or no symptoms. Immunocompromised patients and patients with co-morbidities are more vulnerable to have more aggressive affection with higher rate of complications. Thus, cancer patients carry a higher risk of infection. Diseased patient can transmit infection throughout the disease course starting from the incubation period to clinical recovery. All healthcare workers contacting COVID-19-positive patients are at great risk of infection, especially the anesthesiologists who can be exposed to high viral load during airway manipulation. In the National Cancer Institute of Egypt, we apply a protocol to prioritize cases where elective cancer surgeries that would not affect patient prognosis and outcome are postponed during the early phase and peak of the pandemic till reaching a plateau. However, emergency and urgent surgeries that can compromise cancer patient's life and prognosis take place after the proper assessment of the patient's condition. Aim This review aims to spot the management of cancer patients undergoing surgery during the COVID-19 pandemic in the National Cancer Institute, Egypt.